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IBC 2025 Compression Recap: InterDigital’s Film-Grain VVC, Vecima’s KeyFrame AI, and What’s Next for SimaBit
IBC 2025 Compression Recap: InterDigital's Film-Grain VVC, Vecima's KeyFrame AI, and What's Next for SimaBit
The Amsterdam RAI Convention Centre buzzed with innovation from September 12-15, 2025, as IBC 2025 showcased the latest breakthroughs in video compression and streaming technology. This year's event highlighted three major developments that are reshaping how we think about video quality, bandwidth efficiency, and AI-driven optimization. From InterDigital's groundbreaking VVC film-grain synthesis to Vecima's real-time KeyFrame upscaling technology, and Synamedia's comprehensive AI encoder evaluation suite, the industry is clearly moving toward more intelligent, efficient video processing solutions.
The convergence of artificial intelligence and video compression has reached a tipping point, with companies demonstrating practical applications that deliver measurable improvements in both quality and efficiency. These advances come at a critical time when streaming services face mounting pressure to reduce bandwidth costs while maintaining exceptional viewer experiences across diverse content types and viewing conditions.
The State of Video Compression in 2025
Video compression technology has evolved dramatically over the past year, with AI-driven preprocessing engines leading the charge in bandwidth reduction and quality enhancement. Modern streaming platforms are increasingly adopting codec-agnostic solutions that can optimize content before it reaches traditional encoders like H.264, HEVC, AV1, or emerging standards like AV2 (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
The industry has witnessed significant improvements in edge AI performance, with companies achieving up to 85% greater efficiency compared to leading competitors in recent MLPerf benchmarks (Breaking New Ground: SiMa.ai's Unprecedented Advances in MLPerf™ Benchmarks). These advances in processing efficiency directly translate to more sophisticated real-time video optimization capabilities.
Streaming providers are particularly focused on solutions that can handle diverse content types, from traditional Hollywood productions to user-generated content and AI-generated videos. The challenge of maintaining quality across this spectrum while minimizing bandwidth usage has become a key differentiator in the competitive streaming landscape (Midjourney AI Video on Social Media: Fixing AI Video Quality).
InterDigital's VVC Film-Grain Synthesis: A New Standard
InterDigital's demonstration of VVC (Versatile Video Coding) with integrated film-grain synthesis represents a significant leap forward in video compression technology. This approach addresses one of the most challenging aspects of video encoding: preserving the natural texture and grain that gives content its cinematic quality while achieving maximum compression efficiency.
The film-grain synthesis technology works by analyzing the grain characteristics of source material and encoding these patterns as metadata rather than pixel data. During playback, the decoder reconstructs the grain pattern, maintaining the visual authenticity of the original content while dramatically reducing the bitrate required for transmission. This technique is particularly valuable for streaming services that distribute high-quality film content where grain preservation is essential for viewer satisfaction.
Previous IBC sessions have highlighted the importance of encoder optimizations and film grain handling in modern video coding standards (Technical Papers 2024 Session: Advances in Video Coding – encoder optimisations and film grain). InterDigital's implementation builds on these foundations, offering a practical solution that can be integrated into existing VVC workflows.
The implications for streaming providers are substantial. By maintaining grain authenticity while reducing bandwidth requirements, this technology enables the delivery of premium content experiences without the traditional trade-offs between quality and efficiency. Content creators can preserve their artistic vision while distributors benefit from reduced CDN costs and improved streaming performance.
Vecima's KeyFrame AI: Real-Time Upscaling Revolution
Vecima Networks showcased their exclusive partnership with Digital Harmonic's KeyFrame Media Optimization Solution, demonstrating real-time AI-driven video enhancement that represents a paradigm shift in content delivery optimization. The KeyFrame technology uses generative artificial intelligence to optimize every frame while dramatically reducing bitrate requirements (Vecima Named Exclusive Global Partner for Digital Harmonic's dh/KeyFrame™ Media Optimization Solution).
The KeyFrame solution addresses multiple quality challenges simultaneously, including denoising, artifact removal, spatial and temporal anti-aliasing, and artifact-free upscaling. This comprehensive approach ensures true 1080p and 4K delivery quality regardless of the source material's original resolution or condition. The technology's real-time processing capabilities make it particularly valuable for live streaming applications where traditional post-processing workflows are not feasible.
Vecima's demonstration highlighted the solution's ability to optimize content quality while reducing bandwidth requirements, a combination that directly addresses the dual pressures facing modern streaming services. The AI-driven approach adapts to different content types and viewing conditions, providing consistent quality improvements across diverse scenarios (Vecima to Highlight AI-Driven Content Quality, Efficiency, and Performance at NAB Show 2025).
The technology's codec-agnostic design means it can integrate with existing encoding workflows without requiring wholesale infrastructure changes. This compatibility is crucial for streaming providers who have invested heavily in current systems but need to improve efficiency and quality to remain competitive.
Synamedia's AI Encoder Evaluation Suite: Measuring What Matters
Synamedia's AI encoder evaluation suite provides the industry with sophisticated tools for measuring and comparing the performance of different AI-enhanced encoding solutions. This development addresses a critical need in the market: objective, standardized methods for evaluating the effectiveness of AI-driven video optimization technologies.
The evaluation suite incorporates multiple quality metrics beyond traditional PSNR measurements, including perceptual quality assessments that better reflect human visual perception. This comprehensive approach to quality measurement is essential as the industry moves toward AI-enhanced encoding solutions that may optimize for perceptual quality rather than mathematical precision.
For streaming providers evaluating different AI preprocessing solutions, having standardized benchmarking tools is invaluable. The suite enables direct comparisons between technologies like SimaBit's AI preprocessing engine and other market solutions, providing objective data to support technology adoption decisions (Boost Video Quality Before Compression).
The evaluation framework also considers real-world deployment scenarios, including varying network conditions, device capabilities, and content types. This practical approach ensures that benchmark results translate to actual performance improvements in production environments.
The Role of AI Preprocessing in Modern Streaming
AI preprocessing has emerged as a critical component in modern video streaming workflows, offering significant advantages over traditional encoding approaches. These solutions work by analyzing and optimizing video content before it reaches standard encoders, enabling better compression efficiency and quality preservation across different codec standards.
The effectiveness of AI preprocessing is particularly evident when dealing with challenging content types, such as user-generated videos or AI-generated content that may contain artifacts or quality issues. By addressing these problems before encoding, preprocessing engines can achieve better overall results than post-processing approaches (Midjourney AI Video on Social Media: Fixing AI Video Quality).
Modern AI preprocessing solutions are designed to be codec-agnostic, meaning they can enhance the performance of any downstream encoder. This flexibility is crucial for streaming providers who may use different codecs for different use cases or who want to maintain the ability to adopt new encoding standards as they emerge (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
The business impact of effective AI preprocessing can be substantial, with some solutions demonstrating bandwidth reductions of 22% or more while simultaneously improving perceptual quality. These improvements translate directly to reduced CDN costs and better viewer experiences, making AI preprocessing an attractive investment for streaming providers.
Industry Trends and Future Directions
The IBC 2025 demonstrations reflect several key trends shaping the video compression industry. First, there's a clear movement toward AI-enhanced solutions that can adapt to content characteristics and viewing conditions in real-time. This adaptability is crucial as content diversity continues to expand and viewing patterns become more complex.
Second, the industry is embracing codec-agnostic approaches that provide flexibility and future-proofing. Rather than betting on specific encoding standards, leading companies are developing solutions that can enhance any encoder's performance, providing insurance against technological obsolescence.
Third, there's increasing focus on perceptual quality metrics that better reflect human visual perception. Traditional mathematical quality measures are being supplemented or replaced by AI-driven assessments that correlate more closely with viewer satisfaction.
The integration of edge AI capabilities is also accelerating, with recent MLPerf benchmark results showing consistent improvements in performance and efficiency (SiMa.ai secures best-in-class MLPerf benchmark results for three consecutive submissions). These advances enable more sophisticated real-time processing capabilities that were previously impractical.
Practical Implementation Considerations
For streaming providers considering the adoption of advanced compression technologies demonstrated at IBC 2025, several practical factors deserve consideration. Integration complexity varies significantly between solutions, with some requiring minimal changes to existing workflows while others may necessitate more substantial infrastructure modifications.
Cost-benefit analysis should consider both immediate bandwidth savings and long-term scalability. Solutions that provide significant bandwidth reduction can quickly pay for themselves through reduced CDN costs, but the calculation becomes more complex when factoring in implementation costs and ongoing operational requirements.
Content type compatibility is another crucial consideration. Some optimization techniques work better with specific content types, so providers with diverse content libraries need solutions that can handle everything from live sports to animated content to user-generated videos effectively (5 Must-Have AI Tools to Streamline Your Business).
Quality assurance processes may need updating to accommodate AI-enhanced encoding workflows. Traditional quality control methods may not be sufficient for evaluating the output of AI preprocessing systems, requiring new testing methodologies and quality metrics.
The Competitive Landscape
The video compression market is experiencing rapid innovation, with multiple approaches competing for adoption. Hardware-based solutions offer high performance but may lack flexibility, while software-based approaches provide adaptability at the cost of processing efficiency.
Cloud-based processing solutions are gaining traction, offering scalability and reduced infrastructure requirements. However, latency considerations make edge processing attractive for real-time applications, driving continued innovation in efficient edge AI implementations (Model Browser).
The market is also seeing increased collaboration between technology providers, with partnerships like Vecima's exclusive arrangement with Digital Harmonic demonstrating how companies are combining complementary technologies to deliver comprehensive solutions.
Standardization efforts are ongoing, but the rapid pace of innovation means that proprietary solutions often lead standards development. This dynamic creates both opportunities and risks for technology adopters who must balance cutting-edge capabilities with long-term compatibility.
Quality Enhancement Beyond Compression
While compression efficiency remains a primary focus, the industry is increasingly recognizing the importance of quality enhancement capabilities that go beyond simple bitrate reduction. Modern AI-driven solutions can actually improve the perceptual quality of content while reducing bandwidth requirements, creating a win-win scenario for providers and viewers.
These quality improvements are particularly valuable for older content that may have been encoded with less sophisticated techniques or content that has been degraded through multiple encoding passes. AI preprocessing can restore detail and reduce artifacts, effectively remastering content for modern distribution (Boost Video Quality Before Compression).
The ability to enhance quality while reducing bandwidth is especially important for mobile streaming scenarios where network conditions may be challenging. By optimizing content for these conditions before encoding, providers can maintain acceptable quality levels even when bandwidth is limited.
Real-time quality adaptation is another emerging capability, with AI systems able to adjust optimization parameters based on current network conditions and device capabilities. This dynamic approach ensures optimal viewing experiences across diverse scenarios without requiring multiple encoded versions of the same content.
Looking Ahead: What's Next for Video Compression
The technologies demonstrated at IBC 2025 represent just the beginning of a broader transformation in video compression and delivery. As AI capabilities continue to advance, we can expect even more sophisticated optimization techniques that can understand content semantics and viewer preferences to deliver truly personalized streaming experiences.
The integration of generative AI techniques, similar to those used in KeyFrame's upscaling technology, may enable new approaches to content enhancement and compression. These techniques could potentially reconstruct high-quality video from extremely compressed representations, pushing the boundaries of what's possible in bandwidth-constrained environments.
Edge computing capabilities will continue to improve, enabling more sophisticated real-time processing at the network edge. This evolution will reduce latency and enable new interactive streaming experiences that require immediate response to viewer actions or preferences.
The development of new quality metrics that better reflect human perception will drive further optimization of AI-enhanced encoding systems. As our understanding of visual perception improves, encoding systems will become more effective at preserving the aspects of video quality that matter most to viewers.
Conclusion
IBC 2025 showcased a video compression industry in the midst of a significant transformation, with AI-driven technologies leading the charge toward more efficient and higher-quality streaming experiences. InterDigital's VVC film-grain synthesis, Vecima's KeyFrame AI upscaling, and Synamedia's evaluation suite represent different aspects of this evolution, each addressing specific challenges in modern video delivery.
The convergence of these technologies points toward a future where streaming providers can deliver exceptional quality experiences while minimizing bandwidth costs and infrastructure requirements. The codec-agnostic approach adopted by many solutions provides flexibility and future-proofing, ensuring that investments in optimization technology will remain valuable as encoding standards continue to evolve.
For streaming providers, the message from IBC 2025 is clear: AI-enhanced video processing is no longer experimental technology but a practical necessity for remaining competitive in an increasingly demanding market. The companies that successfully integrate these advanced optimization techniques will be best positioned to deliver the quality experiences that viewers expect while maintaining the operational efficiency that business success requires (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
As the industry continues to evolve, the focus will likely shift from simply reducing bandwidth to creating more intelligent, adaptive streaming systems that can optimize for multiple objectives simultaneously. The technologies demonstrated at IBC 2025 provide a foundation for this next phase of innovation, promising even more exciting developments in the years ahead.
Frequently Asked Questions
What is InterDigital's VVC film-grain synthesis technology showcased at IBC 2025?
InterDigital's VVC film-grain synthesis represents a major advancement in video compression that preserves the natural film grain texture while significantly reducing bitrate requirements. This technology uses advanced algorithms to analyze and recreate film grain patterns during playback, maintaining cinematic quality without storing the actual grain data. The innovation builds on previous IBC presentations focusing on encoder optimizations and film grain handling, offering broadcasters and streaming services a way to deliver premium visual experiences with improved bandwidth efficiency.
How does Vecima's KeyFrame AI upscaling technology work?
Vecima's KeyFrame AI technology uses real-time generative artificial intelligence to optimize video quality while dramatically reducing bitrate requirements. As the exclusive global provider of Digital Harmonic's KeyFrame Media Optimization Solution, Vecima offers patented technology that ensures true 1080p and 4K quality through denoising, artifact removal, spatial and temporal anti-aliasing, and artifact-free upscaling. The AI-driven system optimizes every frame with exceptional efficiency, delivering superior streaming quality while reducing operational costs for content providers.
What makes SiMa.ai's MLPerf benchmark performance significant for video compression?
SiMa.ai has achieved unprecedented results in MLPerf benchmarks, demonstrating up to 85% greater efficiency compared to leading competitors and securing best-in-class results for three consecutive submissions. Their custom-made ML Accelerator and Palette software have delivered 7-16% performance improvements across all workloads, with a 20% improvement in MLPerf Closed Edge Power scores. This makes SiMa.ai the first startup to beat established ML leaders like NVIDIA in inference benchmarks, positioning them as a key player in AI-driven video processing and compression optimization.
How can AI video codecs help with bandwidth reduction for streaming services?
AI video codecs leverage machine learning algorithms to analyze video content in real-time and apply intelligent compression techniques that traditional codecs cannot achieve. These systems can predict and reconstruct video frames more efficiently, reduce redundant data, and optimize bitrate allocation based on content complexity. By understanding visual patterns and viewer perception, AI codecs can maintain high quality while significantly reducing bandwidth requirements, making streaming more cost-effective and accessible across various network conditions.
What are the key applications of edge AI in video processing demonstrated at IBC 2025?
Edge AI applications in video processing span multiple sectors including smart vision, automotive, industrial robotics, healthcare, drones, government, and smart retail. SiMa.ai's MLSoC product family and development tools enable real-time video analysis, quality enhancement, and compression optimization directly at the edge. This reduces latency, minimizes bandwidth usage, and enables privacy-preserving video processing for applications ranging from autonomous vehicles to medical imaging and surveillance systems.
How do modern AI video enhancement techniques address quality issues in social media content?
Modern AI video enhancement techniques tackle common social media video quality problems through advanced upscaling, denoising, and artifact removal algorithms. These systems can fix compression artifacts, improve resolution, stabilize shaky footage, and enhance color accuracy in real-time. AI-powered solutions like those demonstrated at IBC 2025 enable content creators to maintain professional quality standards even when working with lower-quality source material or when content needs to be optimized for various social media platforms with different compression requirements.
Sources
https://sima.ai/blog/breaking-new-ground-sima-ais-unprecedented-advances-in-mlperf-benchmarks/
https://sima.ai/blog/sima-secures-best-in-class-mlperf-benchmark/
https://www.sima.live/blog/5-must-have-ai-tools-to-streamline-your-business
https://www.sima.live/blog/boost-video-quality-before-compression
https://www.sima.live/blog/midjourney-ai-video-on-social-media-fixing-ai-video-quality
https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec
IBC 2025 Compression Recap: InterDigital's Film-Grain VVC, Vecima's KeyFrame AI, and What's Next for SimaBit
The Amsterdam RAI Convention Centre buzzed with innovation from September 12-15, 2025, as IBC 2025 showcased the latest breakthroughs in video compression and streaming technology. This year's event highlighted three major developments that are reshaping how we think about video quality, bandwidth efficiency, and AI-driven optimization. From InterDigital's groundbreaking VVC film-grain synthesis to Vecima's real-time KeyFrame upscaling technology, and Synamedia's comprehensive AI encoder evaluation suite, the industry is clearly moving toward more intelligent, efficient video processing solutions.
The convergence of artificial intelligence and video compression has reached a tipping point, with companies demonstrating practical applications that deliver measurable improvements in both quality and efficiency. These advances come at a critical time when streaming services face mounting pressure to reduce bandwidth costs while maintaining exceptional viewer experiences across diverse content types and viewing conditions.
The State of Video Compression in 2025
Video compression technology has evolved dramatically over the past year, with AI-driven preprocessing engines leading the charge in bandwidth reduction and quality enhancement. Modern streaming platforms are increasingly adopting codec-agnostic solutions that can optimize content before it reaches traditional encoders like H.264, HEVC, AV1, or emerging standards like AV2 (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
The industry has witnessed significant improvements in edge AI performance, with companies achieving up to 85% greater efficiency compared to leading competitors in recent MLPerf benchmarks (Breaking New Ground: SiMa.ai's Unprecedented Advances in MLPerf™ Benchmarks). These advances in processing efficiency directly translate to more sophisticated real-time video optimization capabilities.
Streaming providers are particularly focused on solutions that can handle diverse content types, from traditional Hollywood productions to user-generated content and AI-generated videos. The challenge of maintaining quality across this spectrum while minimizing bandwidth usage has become a key differentiator in the competitive streaming landscape (Midjourney AI Video on Social Media: Fixing AI Video Quality).
InterDigital's VVC Film-Grain Synthesis: A New Standard
InterDigital's demonstration of VVC (Versatile Video Coding) with integrated film-grain synthesis represents a significant leap forward in video compression technology. This approach addresses one of the most challenging aspects of video encoding: preserving the natural texture and grain that gives content its cinematic quality while achieving maximum compression efficiency.
The film-grain synthesis technology works by analyzing the grain characteristics of source material and encoding these patterns as metadata rather than pixel data. During playback, the decoder reconstructs the grain pattern, maintaining the visual authenticity of the original content while dramatically reducing the bitrate required for transmission. This technique is particularly valuable for streaming services that distribute high-quality film content where grain preservation is essential for viewer satisfaction.
Previous IBC sessions have highlighted the importance of encoder optimizations and film grain handling in modern video coding standards (Technical Papers 2024 Session: Advances in Video Coding – encoder optimisations and film grain). InterDigital's implementation builds on these foundations, offering a practical solution that can be integrated into existing VVC workflows.
The implications for streaming providers are substantial. By maintaining grain authenticity while reducing bandwidth requirements, this technology enables the delivery of premium content experiences without the traditional trade-offs between quality and efficiency. Content creators can preserve their artistic vision while distributors benefit from reduced CDN costs and improved streaming performance.
Vecima's KeyFrame AI: Real-Time Upscaling Revolution
Vecima Networks showcased their exclusive partnership with Digital Harmonic's KeyFrame Media Optimization Solution, demonstrating real-time AI-driven video enhancement that represents a paradigm shift in content delivery optimization. The KeyFrame technology uses generative artificial intelligence to optimize every frame while dramatically reducing bitrate requirements (Vecima Named Exclusive Global Partner for Digital Harmonic's dh/KeyFrame™ Media Optimization Solution).
The KeyFrame solution addresses multiple quality challenges simultaneously, including denoising, artifact removal, spatial and temporal anti-aliasing, and artifact-free upscaling. This comprehensive approach ensures true 1080p and 4K delivery quality regardless of the source material's original resolution or condition. The technology's real-time processing capabilities make it particularly valuable for live streaming applications where traditional post-processing workflows are not feasible.
Vecima's demonstration highlighted the solution's ability to optimize content quality while reducing bandwidth requirements, a combination that directly addresses the dual pressures facing modern streaming services. The AI-driven approach adapts to different content types and viewing conditions, providing consistent quality improvements across diverse scenarios (Vecima to Highlight AI-Driven Content Quality, Efficiency, and Performance at NAB Show 2025).
The technology's codec-agnostic design means it can integrate with existing encoding workflows without requiring wholesale infrastructure changes. This compatibility is crucial for streaming providers who have invested heavily in current systems but need to improve efficiency and quality to remain competitive.
Synamedia's AI Encoder Evaluation Suite: Measuring What Matters
Synamedia's AI encoder evaluation suite provides the industry with sophisticated tools for measuring and comparing the performance of different AI-enhanced encoding solutions. This development addresses a critical need in the market: objective, standardized methods for evaluating the effectiveness of AI-driven video optimization technologies.
The evaluation suite incorporates multiple quality metrics beyond traditional PSNR measurements, including perceptual quality assessments that better reflect human visual perception. This comprehensive approach to quality measurement is essential as the industry moves toward AI-enhanced encoding solutions that may optimize for perceptual quality rather than mathematical precision.
For streaming providers evaluating different AI preprocessing solutions, having standardized benchmarking tools is invaluable. The suite enables direct comparisons between technologies like SimaBit's AI preprocessing engine and other market solutions, providing objective data to support technology adoption decisions (Boost Video Quality Before Compression).
The evaluation framework also considers real-world deployment scenarios, including varying network conditions, device capabilities, and content types. This practical approach ensures that benchmark results translate to actual performance improvements in production environments.
The Role of AI Preprocessing in Modern Streaming
AI preprocessing has emerged as a critical component in modern video streaming workflows, offering significant advantages over traditional encoding approaches. These solutions work by analyzing and optimizing video content before it reaches standard encoders, enabling better compression efficiency and quality preservation across different codec standards.
The effectiveness of AI preprocessing is particularly evident when dealing with challenging content types, such as user-generated videos or AI-generated content that may contain artifacts or quality issues. By addressing these problems before encoding, preprocessing engines can achieve better overall results than post-processing approaches (Midjourney AI Video on Social Media: Fixing AI Video Quality).
Modern AI preprocessing solutions are designed to be codec-agnostic, meaning they can enhance the performance of any downstream encoder. This flexibility is crucial for streaming providers who may use different codecs for different use cases or who want to maintain the ability to adopt new encoding standards as they emerge (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
The business impact of effective AI preprocessing can be substantial, with some solutions demonstrating bandwidth reductions of 22% or more while simultaneously improving perceptual quality. These improvements translate directly to reduced CDN costs and better viewer experiences, making AI preprocessing an attractive investment for streaming providers.
Industry Trends and Future Directions
The IBC 2025 demonstrations reflect several key trends shaping the video compression industry. First, there's a clear movement toward AI-enhanced solutions that can adapt to content characteristics and viewing conditions in real-time. This adaptability is crucial as content diversity continues to expand and viewing patterns become more complex.
Second, the industry is embracing codec-agnostic approaches that provide flexibility and future-proofing. Rather than betting on specific encoding standards, leading companies are developing solutions that can enhance any encoder's performance, providing insurance against technological obsolescence.
Third, there's increasing focus on perceptual quality metrics that better reflect human visual perception. Traditional mathematical quality measures are being supplemented or replaced by AI-driven assessments that correlate more closely with viewer satisfaction.
The integration of edge AI capabilities is also accelerating, with recent MLPerf benchmark results showing consistent improvements in performance and efficiency (SiMa.ai secures best-in-class MLPerf benchmark results for three consecutive submissions). These advances enable more sophisticated real-time processing capabilities that were previously impractical.
Practical Implementation Considerations
For streaming providers considering the adoption of advanced compression technologies demonstrated at IBC 2025, several practical factors deserve consideration. Integration complexity varies significantly between solutions, with some requiring minimal changes to existing workflows while others may necessitate more substantial infrastructure modifications.
Cost-benefit analysis should consider both immediate bandwidth savings and long-term scalability. Solutions that provide significant bandwidth reduction can quickly pay for themselves through reduced CDN costs, but the calculation becomes more complex when factoring in implementation costs and ongoing operational requirements.
Content type compatibility is another crucial consideration. Some optimization techniques work better with specific content types, so providers with diverse content libraries need solutions that can handle everything from live sports to animated content to user-generated videos effectively (5 Must-Have AI Tools to Streamline Your Business).
Quality assurance processes may need updating to accommodate AI-enhanced encoding workflows. Traditional quality control methods may not be sufficient for evaluating the output of AI preprocessing systems, requiring new testing methodologies and quality metrics.
The Competitive Landscape
The video compression market is experiencing rapid innovation, with multiple approaches competing for adoption. Hardware-based solutions offer high performance but may lack flexibility, while software-based approaches provide adaptability at the cost of processing efficiency.
Cloud-based processing solutions are gaining traction, offering scalability and reduced infrastructure requirements. However, latency considerations make edge processing attractive for real-time applications, driving continued innovation in efficient edge AI implementations (Model Browser).
The market is also seeing increased collaboration between technology providers, with partnerships like Vecima's exclusive arrangement with Digital Harmonic demonstrating how companies are combining complementary technologies to deliver comprehensive solutions.
Standardization efforts are ongoing, but the rapid pace of innovation means that proprietary solutions often lead standards development. This dynamic creates both opportunities and risks for technology adopters who must balance cutting-edge capabilities with long-term compatibility.
Quality Enhancement Beyond Compression
While compression efficiency remains a primary focus, the industry is increasingly recognizing the importance of quality enhancement capabilities that go beyond simple bitrate reduction. Modern AI-driven solutions can actually improve the perceptual quality of content while reducing bandwidth requirements, creating a win-win scenario for providers and viewers.
These quality improvements are particularly valuable for older content that may have been encoded with less sophisticated techniques or content that has been degraded through multiple encoding passes. AI preprocessing can restore detail and reduce artifacts, effectively remastering content for modern distribution (Boost Video Quality Before Compression).
The ability to enhance quality while reducing bandwidth is especially important for mobile streaming scenarios where network conditions may be challenging. By optimizing content for these conditions before encoding, providers can maintain acceptable quality levels even when bandwidth is limited.
Real-time quality adaptation is another emerging capability, with AI systems able to adjust optimization parameters based on current network conditions and device capabilities. This dynamic approach ensures optimal viewing experiences across diverse scenarios without requiring multiple encoded versions of the same content.
Looking Ahead: What's Next for Video Compression
The technologies demonstrated at IBC 2025 represent just the beginning of a broader transformation in video compression and delivery. As AI capabilities continue to advance, we can expect even more sophisticated optimization techniques that can understand content semantics and viewer preferences to deliver truly personalized streaming experiences.
The integration of generative AI techniques, similar to those used in KeyFrame's upscaling technology, may enable new approaches to content enhancement and compression. These techniques could potentially reconstruct high-quality video from extremely compressed representations, pushing the boundaries of what's possible in bandwidth-constrained environments.
Edge computing capabilities will continue to improve, enabling more sophisticated real-time processing at the network edge. This evolution will reduce latency and enable new interactive streaming experiences that require immediate response to viewer actions or preferences.
The development of new quality metrics that better reflect human perception will drive further optimization of AI-enhanced encoding systems. As our understanding of visual perception improves, encoding systems will become more effective at preserving the aspects of video quality that matter most to viewers.
Conclusion
IBC 2025 showcased a video compression industry in the midst of a significant transformation, with AI-driven technologies leading the charge toward more efficient and higher-quality streaming experiences. InterDigital's VVC film-grain synthesis, Vecima's KeyFrame AI upscaling, and Synamedia's evaluation suite represent different aspects of this evolution, each addressing specific challenges in modern video delivery.
The convergence of these technologies points toward a future where streaming providers can deliver exceptional quality experiences while minimizing bandwidth costs and infrastructure requirements. The codec-agnostic approach adopted by many solutions provides flexibility and future-proofing, ensuring that investments in optimization technology will remain valuable as encoding standards continue to evolve.
For streaming providers, the message from IBC 2025 is clear: AI-enhanced video processing is no longer experimental technology but a practical necessity for remaining competitive in an increasingly demanding market. The companies that successfully integrate these advanced optimization techniques will be best positioned to deliver the quality experiences that viewers expect while maintaining the operational efficiency that business success requires (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
As the industry continues to evolve, the focus will likely shift from simply reducing bandwidth to creating more intelligent, adaptive streaming systems that can optimize for multiple objectives simultaneously. The technologies demonstrated at IBC 2025 provide a foundation for this next phase of innovation, promising even more exciting developments in the years ahead.
Frequently Asked Questions
What is InterDigital's VVC film-grain synthesis technology showcased at IBC 2025?
InterDigital's VVC film-grain synthesis represents a major advancement in video compression that preserves the natural film grain texture while significantly reducing bitrate requirements. This technology uses advanced algorithms to analyze and recreate film grain patterns during playback, maintaining cinematic quality without storing the actual grain data. The innovation builds on previous IBC presentations focusing on encoder optimizations and film grain handling, offering broadcasters and streaming services a way to deliver premium visual experiences with improved bandwidth efficiency.
How does Vecima's KeyFrame AI upscaling technology work?
Vecima's KeyFrame AI technology uses real-time generative artificial intelligence to optimize video quality while dramatically reducing bitrate requirements. As the exclusive global provider of Digital Harmonic's KeyFrame Media Optimization Solution, Vecima offers patented technology that ensures true 1080p and 4K quality through denoising, artifact removal, spatial and temporal anti-aliasing, and artifact-free upscaling. The AI-driven system optimizes every frame with exceptional efficiency, delivering superior streaming quality while reducing operational costs for content providers.
What makes SiMa.ai's MLPerf benchmark performance significant for video compression?
SiMa.ai has achieved unprecedented results in MLPerf benchmarks, demonstrating up to 85% greater efficiency compared to leading competitors and securing best-in-class results for three consecutive submissions. Their custom-made ML Accelerator and Palette software have delivered 7-16% performance improvements across all workloads, with a 20% improvement in MLPerf Closed Edge Power scores. This makes SiMa.ai the first startup to beat established ML leaders like NVIDIA in inference benchmarks, positioning them as a key player in AI-driven video processing and compression optimization.
How can AI video codecs help with bandwidth reduction for streaming services?
AI video codecs leverage machine learning algorithms to analyze video content in real-time and apply intelligent compression techniques that traditional codecs cannot achieve. These systems can predict and reconstruct video frames more efficiently, reduce redundant data, and optimize bitrate allocation based on content complexity. By understanding visual patterns and viewer perception, AI codecs can maintain high quality while significantly reducing bandwidth requirements, making streaming more cost-effective and accessible across various network conditions.
What are the key applications of edge AI in video processing demonstrated at IBC 2025?
Edge AI applications in video processing span multiple sectors including smart vision, automotive, industrial robotics, healthcare, drones, government, and smart retail. SiMa.ai's MLSoC product family and development tools enable real-time video analysis, quality enhancement, and compression optimization directly at the edge. This reduces latency, minimizes bandwidth usage, and enables privacy-preserving video processing for applications ranging from autonomous vehicles to medical imaging and surveillance systems.
How do modern AI video enhancement techniques address quality issues in social media content?
Modern AI video enhancement techniques tackle common social media video quality problems through advanced upscaling, denoising, and artifact removal algorithms. These systems can fix compression artifacts, improve resolution, stabilize shaky footage, and enhance color accuracy in real-time. AI-powered solutions like those demonstrated at IBC 2025 enable content creators to maintain professional quality standards even when working with lower-quality source material or when content needs to be optimized for various social media platforms with different compression requirements.
Sources
https://sima.ai/blog/breaking-new-ground-sima-ais-unprecedented-advances-in-mlperf-benchmarks/
https://sima.ai/blog/sima-secures-best-in-class-mlperf-benchmark/
https://www.sima.live/blog/5-must-have-ai-tools-to-streamline-your-business
https://www.sima.live/blog/boost-video-quality-before-compression
https://www.sima.live/blog/midjourney-ai-video-on-social-media-fixing-ai-video-quality
https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec
IBC 2025 Compression Recap: InterDigital's Film-Grain VVC, Vecima's KeyFrame AI, and What's Next for SimaBit
The Amsterdam RAI Convention Centre buzzed with innovation from September 12-15, 2025, as IBC 2025 showcased the latest breakthroughs in video compression and streaming technology. This year's event highlighted three major developments that are reshaping how we think about video quality, bandwidth efficiency, and AI-driven optimization. From InterDigital's groundbreaking VVC film-grain synthesis to Vecima's real-time KeyFrame upscaling technology, and Synamedia's comprehensive AI encoder evaluation suite, the industry is clearly moving toward more intelligent, efficient video processing solutions.
The convergence of artificial intelligence and video compression has reached a tipping point, with companies demonstrating practical applications that deliver measurable improvements in both quality and efficiency. These advances come at a critical time when streaming services face mounting pressure to reduce bandwidth costs while maintaining exceptional viewer experiences across diverse content types and viewing conditions.
The State of Video Compression in 2025
Video compression technology has evolved dramatically over the past year, with AI-driven preprocessing engines leading the charge in bandwidth reduction and quality enhancement. Modern streaming platforms are increasingly adopting codec-agnostic solutions that can optimize content before it reaches traditional encoders like H.264, HEVC, AV1, or emerging standards like AV2 (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
The industry has witnessed significant improvements in edge AI performance, with companies achieving up to 85% greater efficiency compared to leading competitors in recent MLPerf benchmarks (Breaking New Ground: SiMa.ai's Unprecedented Advances in MLPerf™ Benchmarks). These advances in processing efficiency directly translate to more sophisticated real-time video optimization capabilities.
Streaming providers are particularly focused on solutions that can handle diverse content types, from traditional Hollywood productions to user-generated content and AI-generated videos. The challenge of maintaining quality across this spectrum while minimizing bandwidth usage has become a key differentiator in the competitive streaming landscape (Midjourney AI Video on Social Media: Fixing AI Video Quality).
InterDigital's VVC Film-Grain Synthesis: A New Standard
InterDigital's demonstration of VVC (Versatile Video Coding) with integrated film-grain synthesis represents a significant leap forward in video compression technology. This approach addresses one of the most challenging aspects of video encoding: preserving the natural texture and grain that gives content its cinematic quality while achieving maximum compression efficiency.
The film-grain synthesis technology works by analyzing the grain characteristics of source material and encoding these patterns as metadata rather than pixel data. During playback, the decoder reconstructs the grain pattern, maintaining the visual authenticity of the original content while dramatically reducing the bitrate required for transmission. This technique is particularly valuable for streaming services that distribute high-quality film content where grain preservation is essential for viewer satisfaction.
Previous IBC sessions have highlighted the importance of encoder optimizations and film grain handling in modern video coding standards (Technical Papers 2024 Session: Advances in Video Coding – encoder optimisations and film grain). InterDigital's implementation builds on these foundations, offering a practical solution that can be integrated into existing VVC workflows.
The implications for streaming providers are substantial. By maintaining grain authenticity while reducing bandwidth requirements, this technology enables the delivery of premium content experiences without the traditional trade-offs between quality and efficiency. Content creators can preserve their artistic vision while distributors benefit from reduced CDN costs and improved streaming performance.
Vecima's KeyFrame AI: Real-Time Upscaling Revolution
Vecima Networks showcased their exclusive partnership with Digital Harmonic's KeyFrame Media Optimization Solution, demonstrating real-time AI-driven video enhancement that represents a paradigm shift in content delivery optimization. The KeyFrame technology uses generative artificial intelligence to optimize every frame while dramatically reducing bitrate requirements (Vecima Named Exclusive Global Partner for Digital Harmonic's dh/KeyFrame™ Media Optimization Solution).
The KeyFrame solution addresses multiple quality challenges simultaneously, including denoising, artifact removal, spatial and temporal anti-aliasing, and artifact-free upscaling. This comprehensive approach ensures true 1080p and 4K delivery quality regardless of the source material's original resolution or condition. The technology's real-time processing capabilities make it particularly valuable for live streaming applications where traditional post-processing workflows are not feasible.
Vecima's demonstration highlighted the solution's ability to optimize content quality while reducing bandwidth requirements, a combination that directly addresses the dual pressures facing modern streaming services. The AI-driven approach adapts to different content types and viewing conditions, providing consistent quality improvements across diverse scenarios (Vecima to Highlight AI-Driven Content Quality, Efficiency, and Performance at NAB Show 2025).
The technology's codec-agnostic design means it can integrate with existing encoding workflows without requiring wholesale infrastructure changes. This compatibility is crucial for streaming providers who have invested heavily in current systems but need to improve efficiency and quality to remain competitive.
Synamedia's AI Encoder Evaluation Suite: Measuring What Matters
Synamedia's AI encoder evaluation suite provides the industry with sophisticated tools for measuring and comparing the performance of different AI-enhanced encoding solutions. This development addresses a critical need in the market: objective, standardized methods for evaluating the effectiveness of AI-driven video optimization technologies.
The evaluation suite incorporates multiple quality metrics beyond traditional PSNR measurements, including perceptual quality assessments that better reflect human visual perception. This comprehensive approach to quality measurement is essential as the industry moves toward AI-enhanced encoding solutions that may optimize for perceptual quality rather than mathematical precision.
For streaming providers evaluating different AI preprocessing solutions, having standardized benchmarking tools is invaluable. The suite enables direct comparisons between technologies like SimaBit's AI preprocessing engine and other market solutions, providing objective data to support technology adoption decisions (Boost Video Quality Before Compression).
The evaluation framework also considers real-world deployment scenarios, including varying network conditions, device capabilities, and content types. This practical approach ensures that benchmark results translate to actual performance improvements in production environments.
The Role of AI Preprocessing in Modern Streaming
AI preprocessing has emerged as a critical component in modern video streaming workflows, offering significant advantages over traditional encoding approaches. These solutions work by analyzing and optimizing video content before it reaches standard encoders, enabling better compression efficiency and quality preservation across different codec standards.
The effectiveness of AI preprocessing is particularly evident when dealing with challenging content types, such as user-generated videos or AI-generated content that may contain artifacts or quality issues. By addressing these problems before encoding, preprocessing engines can achieve better overall results than post-processing approaches (Midjourney AI Video on Social Media: Fixing AI Video Quality).
Modern AI preprocessing solutions are designed to be codec-agnostic, meaning they can enhance the performance of any downstream encoder. This flexibility is crucial for streaming providers who may use different codecs for different use cases or who want to maintain the ability to adopt new encoding standards as they emerge (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
The business impact of effective AI preprocessing can be substantial, with some solutions demonstrating bandwidth reductions of 22% or more while simultaneously improving perceptual quality. These improvements translate directly to reduced CDN costs and better viewer experiences, making AI preprocessing an attractive investment for streaming providers.
Industry Trends and Future Directions
The IBC 2025 demonstrations reflect several key trends shaping the video compression industry. First, there's a clear movement toward AI-enhanced solutions that can adapt to content characteristics and viewing conditions in real-time. This adaptability is crucial as content diversity continues to expand and viewing patterns become more complex.
Second, the industry is embracing codec-agnostic approaches that provide flexibility and future-proofing. Rather than betting on specific encoding standards, leading companies are developing solutions that can enhance any encoder's performance, providing insurance against technological obsolescence.
Third, there's increasing focus on perceptual quality metrics that better reflect human visual perception. Traditional mathematical quality measures are being supplemented or replaced by AI-driven assessments that correlate more closely with viewer satisfaction.
The integration of edge AI capabilities is also accelerating, with recent MLPerf benchmark results showing consistent improvements in performance and efficiency (SiMa.ai secures best-in-class MLPerf benchmark results for three consecutive submissions). These advances enable more sophisticated real-time processing capabilities that were previously impractical.
Practical Implementation Considerations
For streaming providers considering the adoption of advanced compression technologies demonstrated at IBC 2025, several practical factors deserve consideration. Integration complexity varies significantly between solutions, with some requiring minimal changes to existing workflows while others may necessitate more substantial infrastructure modifications.
Cost-benefit analysis should consider both immediate bandwidth savings and long-term scalability. Solutions that provide significant bandwidth reduction can quickly pay for themselves through reduced CDN costs, but the calculation becomes more complex when factoring in implementation costs and ongoing operational requirements.
Content type compatibility is another crucial consideration. Some optimization techniques work better with specific content types, so providers with diverse content libraries need solutions that can handle everything from live sports to animated content to user-generated videos effectively (5 Must-Have AI Tools to Streamline Your Business).
Quality assurance processes may need updating to accommodate AI-enhanced encoding workflows. Traditional quality control methods may not be sufficient for evaluating the output of AI preprocessing systems, requiring new testing methodologies and quality metrics.
The Competitive Landscape
The video compression market is experiencing rapid innovation, with multiple approaches competing for adoption. Hardware-based solutions offer high performance but may lack flexibility, while software-based approaches provide adaptability at the cost of processing efficiency.
Cloud-based processing solutions are gaining traction, offering scalability and reduced infrastructure requirements. However, latency considerations make edge processing attractive for real-time applications, driving continued innovation in efficient edge AI implementations (Model Browser).
The market is also seeing increased collaboration between technology providers, with partnerships like Vecima's exclusive arrangement with Digital Harmonic demonstrating how companies are combining complementary technologies to deliver comprehensive solutions.
Standardization efforts are ongoing, but the rapid pace of innovation means that proprietary solutions often lead standards development. This dynamic creates both opportunities and risks for technology adopters who must balance cutting-edge capabilities with long-term compatibility.
Quality Enhancement Beyond Compression
While compression efficiency remains a primary focus, the industry is increasingly recognizing the importance of quality enhancement capabilities that go beyond simple bitrate reduction. Modern AI-driven solutions can actually improve the perceptual quality of content while reducing bandwidth requirements, creating a win-win scenario for providers and viewers.
These quality improvements are particularly valuable for older content that may have been encoded with less sophisticated techniques or content that has been degraded through multiple encoding passes. AI preprocessing can restore detail and reduce artifacts, effectively remastering content for modern distribution (Boost Video Quality Before Compression).
The ability to enhance quality while reducing bandwidth is especially important for mobile streaming scenarios where network conditions may be challenging. By optimizing content for these conditions before encoding, providers can maintain acceptable quality levels even when bandwidth is limited.
Real-time quality adaptation is another emerging capability, with AI systems able to adjust optimization parameters based on current network conditions and device capabilities. This dynamic approach ensures optimal viewing experiences across diverse scenarios without requiring multiple encoded versions of the same content.
Looking Ahead: What's Next for Video Compression
The technologies demonstrated at IBC 2025 represent just the beginning of a broader transformation in video compression and delivery. As AI capabilities continue to advance, we can expect even more sophisticated optimization techniques that can understand content semantics and viewer preferences to deliver truly personalized streaming experiences.
The integration of generative AI techniques, similar to those used in KeyFrame's upscaling technology, may enable new approaches to content enhancement and compression. These techniques could potentially reconstruct high-quality video from extremely compressed representations, pushing the boundaries of what's possible in bandwidth-constrained environments.
Edge computing capabilities will continue to improve, enabling more sophisticated real-time processing at the network edge. This evolution will reduce latency and enable new interactive streaming experiences that require immediate response to viewer actions or preferences.
The development of new quality metrics that better reflect human perception will drive further optimization of AI-enhanced encoding systems. As our understanding of visual perception improves, encoding systems will become more effective at preserving the aspects of video quality that matter most to viewers.
Conclusion
IBC 2025 showcased a video compression industry in the midst of a significant transformation, with AI-driven technologies leading the charge toward more efficient and higher-quality streaming experiences. InterDigital's VVC film-grain synthesis, Vecima's KeyFrame AI upscaling, and Synamedia's evaluation suite represent different aspects of this evolution, each addressing specific challenges in modern video delivery.
The convergence of these technologies points toward a future where streaming providers can deliver exceptional quality experiences while minimizing bandwidth costs and infrastructure requirements. The codec-agnostic approach adopted by many solutions provides flexibility and future-proofing, ensuring that investments in optimization technology will remain valuable as encoding standards continue to evolve.
For streaming providers, the message from IBC 2025 is clear: AI-enhanced video processing is no longer experimental technology but a practical necessity for remaining competitive in an increasingly demanding market. The companies that successfully integrate these advanced optimization techniques will be best positioned to deliver the quality experiences that viewers expect while maintaining the operational efficiency that business success requires (Understanding Bandwidth Reduction for Streaming with AI Video Codec).
As the industry continues to evolve, the focus will likely shift from simply reducing bandwidth to creating more intelligent, adaptive streaming systems that can optimize for multiple objectives simultaneously. The technologies demonstrated at IBC 2025 provide a foundation for this next phase of innovation, promising even more exciting developments in the years ahead.
Frequently Asked Questions
What is InterDigital's VVC film-grain synthesis technology showcased at IBC 2025?
InterDigital's VVC film-grain synthesis represents a major advancement in video compression that preserves the natural film grain texture while significantly reducing bitrate requirements. This technology uses advanced algorithms to analyze and recreate film grain patterns during playback, maintaining cinematic quality without storing the actual grain data. The innovation builds on previous IBC presentations focusing on encoder optimizations and film grain handling, offering broadcasters and streaming services a way to deliver premium visual experiences with improved bandwidth efficiency.
How does Vecima's KeyFrame AI upscaling technology work?
Vecima's KeyFrame AI technology uses real-time generative artificial intelligence to optimize video quality while dramatically reducing bitrate requirements. As the exclusive global provider of Digital Harmonic's KeyFrame Media Optimization Solution, Vecima offers patented technology that ensures true 1080p and 4K quality through denoising, artifact removal, spatial and temporal anti-aliasing, and artifact-free upscaling. The AI-driven system optimizes every frame with exceptional efficiency, delivering superior streaming quality while reducing operational costs for content providers.
What makes SiMa.ai's MLPerf benchmark performance significant for video compression?
SiMa.ai has achieved unprecedented results in MLPerf benchmarks, demonstrating up to 85% greater efficiency compared to leading competitors and securing best-in-class results for three consecutive submissions. Their custom-made ML Accelerator and Palette software have delivered 7-16% performance improvements across all workloads, with a 20% improvement in MLPerf Closed Edge Power scores. This makes SiMa.ai the first startup to beat established ML leaders like NVIDIA in inference benchmarks, positioning them as a key player in AI-driven video processing and compression optimization.
How can AI video codecs help with bandwidth reduction for streaming services?
AI video codecs leverage machine learning algorithms to analyze video content in real-time and apply intelligent compression techniques that traditional codecs cannot achieve. These systems can predict and reconstruct video frames more efficiently, reduce redundant data, and optimize bitrate allocation based on content complexity. By understanding visual patterns and viewer perception, AI codecs can maintain high quality while significantly reducing bandwidth requirements, making streaming more cost-effective and accessible across various network conditions.
What are the key applications of edge AI in video processing demonstrated at IBC 2025?
Edge AI applications in video processing span multiple sectors including smart vision, automotive, industrial robotics, healthcare, drones, government, and smart retail. SiMa.ai's MLSoC product family and development tools enable real-time video analysis, quality enhancement, and compression optimization directly at the edge. This reduces latency, minimizes bandwidth usage, and enables privacy-preserving video processing for applications ranging from autonomous vehicles to medical imaging and surveillance systems.
How do modern AI video enhancement techniques address quality issues in social media content?
Modern AI video enhancement techniques tackle common social media video quality problems through advanced upscaling, denoising, and artifact removal algorithms. These systems can fix compression artifacts, improve resolution, stabilize shaky footage, and enhance color accuracy in real-time. AI-powered solutions like those demonstrated at IBC 2025 enable content creators to maintain professional quality standards even when working with lower-quality source material or when content needs to be optimized for various social media platforms with different compression requirements.
Sources
https://sima.ai/blog/breaking-new-ground-sima-ais-unprecedented-advances-in-mlperf-benchmarks/
https://sima.ai/blog/sima-secures-best-in-class-mlperf-benchmark/
https://www.sima.live/blog/5-must-have-ai-tools-to-streamline-your-business
https://www.sima.live/blog/boost-video-quality-before-compression
https://www.sima.live/blog/midjourney-ai-video-on-social-media-fixing-ai-video-quality
https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec
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©2025 Sima Labs. All rights reserved
SimaLabs
©2025 Sima Labs. All rights reserved
SimaLabs
©2025 Sima Labs. All rights reserved