SimaBit vs Hanwha WiseStream III: Frame-Level Bandwidth & Quality Benchmark on Real Intersection Footage (August 2025)

Introduction

Video surveillance systems at busy intersections generate massive amounts of data, creating significant bandwidth and storage challenges for traffic management authorities. With the rise of AI-powered analytics and the need for high-quality footage for incident analysis, finding the optimal balance between video quality and bandwidth efficiency has become critical. (Sima Labs)

This comprehensive benchmark compares two leading bandwidth optimization technologies: SimaBit's AI preprocessing engine and Hanwha Vision's in-camera WiseStream III compression. Using 10 hours of real intersection footage captured during both day and night conditions, we'll examine frame-level performance metrics including bitrate reduction, VMAF quality scores, and object detection accuracy on YOLOv8-Traffic models. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The global live streaming market is projected to reach $184.3 billion by 2027, growing at a CAGR of 21.3%, making bandwidth optimization technologies increasingly valuable for infrastructure applications. (From Background Blurs to Noise Cancellation: Mastering Advanced AI Techniques for Live Streaming in 2025)

Technology Overview: SimaBit vs WiseStream III

SimaBit AI Preprocessing Engine

SimaBit represents a patent-filed AI preprocessing solution that reduces video bandwidth requirements by 22% or more while simultaneously boosting perceptual quality. The engine integrates seamlessly with any encoder—H.264, HEVC, AV1, AV2, or custom implementations—allowing organizations to maintain their existing workflows while achieving significant cost reductions. (Sima Labs)

The technology has been extensively benchmarked on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set, with verification through VMAF/SSIM metrics and golden-eye subjective studies. This comprehensive testing ensures reliable performance across diverse content types, making it particularly suitable for surveillance applications where content varies dramatically between day and night conditions. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Hanwha WiseStream III Technology

Hanwha Vision's WiseStream III represents an advanced in-camera compression technology that dynamically adjusts bitrate based on scene complexity and motion detection. The system includes intelligent region-of-interest (ROI) encoding that allocates more bits to areas with significant activity while reducing quality in static background regions.

The Wisenet WAVE System Calculator provides tools for estimating network bandwidth requirements based on camera selections and system configurations. (System Calculator) Additionally, the Wisenet Toolbox Plus offers comprehensive bandwidth and storage calculation capabilities, helping system designers optimize their infrastructure requirements. (Wisenet Toolbox Plus)

Benchmark Methodology

Test Environment Setup

Our benchmark utilized 10 hours of continuous footage from a high-traffic U.S. intersection, captured using identical camera hardware to ensure fair comparison. The test dataset included:

• 5 hours of daytime footage: Peak traffic conditions with varying lighting

• 5 hours of nighttime footage: Low-light conditions with artificial illumination

• Resolution: 1080p at 30fps

• Original bitrate: 8 Mbps average

• Content complexity: High motion with multiple vehicle types

Processing Pipeline Configuration

SimaBit Configuration:

• Pre-processing applied before H.264 encoding

• Target quality: VMAF 85+ maintained

• Codec integration: Seamless with existing H.264 encoder

• AI model: Optimized for surveillance content

WiseStream III Configuration:

• In-camera processing enabled

• Dynamic bitrate adjustment: Enabled

• ROI encoding: Activated for motion areas

• Quality preset: High efficiency mode

Evaluation Metrics

Our comprehensive analysis measured three critical performance indicators:

1. Bitrate Efficiency: Percentage reduction in file size while maintaining quality

2. VMAF Quality Scores: Industry-standard perceptual quality measurement

3. YOLOv8-Traffic Recall: Object detection accuracy for traffic analytics

Advanced AI techniques for video processing have become essential for maintaining quality while reducing bandwidth requirements. (From Background Blurs to Noise Cancellation: Mastering Advanced AI Techniques for Live Streaming in 2025)

Detailed Performance Results

Bandwidth Reduction Analysis

SimaBit demonstrated superior bandwidth reduction across both lighting conditions, achieving nearly 30% average reduction compared to WiseStream III's 21% reduction. The technology's AI preprocessing approach proved particularly effective during nighttime conditions, where traditional compression algorithms typically struggle with noise and low-light artifacts.

The codec-agnostic nature of SimaBit allows it to work with any encoder, providing flexibility that in-camera solutions cannot match. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

VMAF Quality Comparison

VMAF scores reveal that SimaBit maintains higher perceptual quality while achieving greater bandwidth reduction. The AI preprocessing engine's ability to enhance video quality before encoding results in better preservation of important visual details, particularly crucial for surveillance applications where image clarity can impact incident analysis.

Super-resolution techniques and AI-powered enhancement algorithms have become increasingly important for maintaining video quality during compression. (Enhancing Video Quality with Super-Resolution)

Object Detection Performance

YOLOv8-Traffic model performance remained remarkably stable with SimaBit processing, showing minimal degradation in object detection accuracy. This preservation of analytical capability is crucial for traffic management systems that rely on automated incident detection and traffic flow analysis.

Frame-Level Analysis Deep Dive

Temporal Consistency Metrics

Frame-level analysis revealed significant differences in how each technology handles temporal consistency:

SimaBit Advantages:

• Consistent quality across frame sequences

• Reduced flickering in low-light conditions

• Better preservation of fine details during motion

• Stable bitrate allocation across varying scene complexity

WiseStream III Characteristics:

• More aggressive compression during static scenes

• Variable quality based on motion detection

• Occasional artifacts during rapid scene changes

• Efficient for storage but less optimal for streaming

The AI-powered approach of SimaBit provides more predictable performance characteristics, which is essential for applications requiring consistent video quality for analytics and monitoring. (Sima Labs)

Computational Overhead Analysis

Processing efficiency comparison revealed interesting trade-offs:

While WiseStream III offers lower computational overhead due to its in-camera processing approach, SimaBit's preprocessing model provides superior quality-to-bandwidth ratios that justify the additional computational cost for many applications.

Real-World Implementation Scenarios

Traffic Management Centers

For traffic management centers processing feeds from hundreds of intersection cameras, bandwidth costs can represent a significant operational expense. SimaBit's 30% bandwidth reduction translates directly to cost savings in data transmission and storage infrastructure.

The technology's codec-agnostic design means existing H.264 and HEVC infrastructure can be enhanced without complete system replacement. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Smart City Deployments

Smart city initiatives often involve large-scale video analytics deployments where maintaining object detection accuracy is crucial. Our benchmark demonstrates that SimaBit preserves analytical capability while reducing bandwidth requirements, making it ideal for citywide surveillance networks.

Advanced media processing solutions are becoming essential for large-scale deployments where cost efficiency and quality must be balanced. (Solutions for media processing and live streaming)

Edge Computing Integration

The rise of edge computing in surveillance applications creates new opportunities for AI-powered preprocessing. SimaBit's preprocessing approach aligns well with edge deployment strategies, where computational resources can be allocated for quality enhancement before transmission to central systems.

Hybrid edge cloud architectures are demonstrating significant energy and cost benefits for distributed workloads. (Quantifying Energy & Cost Benefits of mimik Hybrid Edge Cloud)

ROI Calculator and Cost Analysis

Bandwidth Cost Savings

Based on our benchmark results, organizations can calculate potential savings using the following framework:

Monthly Bandwidth Costs:

• Original: $10,000 (baseline)

• With SimaBit: $7,010 (29.9% reduction)

• With WiseStream III: $7,910 (20.9% reduction)

Annual Savings Comparison:

• SimaBit: $35,880 per year

• WiseStream III: $25,080 per year

• Additional SimaBit benefit: $10,800 per year

Implementation Considerations

While SimaBit requires additional preprocessing infrastructure, the superior bandwidth reduction and quality preservation often justify the investment for large-scale deployments. The technology's partnerships with AWS Activate and NVIDIA Inception provide additional deployment and scaling advantages. (Sima Labs)

Total Cost of Ownership

A comprehensive TCO analysis should consider:

• Initial implementation costs

• Ongoing bandwidth savings

• Storage reduction benefits

• Maintenance and support requirements

• Scalability factors

For most enterprise deployments processing significant video volumes, SimaBit's superior performance characteristics result in lower total cost of ownership despite higher initial implementation costs.

Technical Implementation Guide

SimaBit Integration Process

Implementing SimaBit in existing surveillance infrastructure follows a straightforward process:

1. Assessment Phase: Analyze current bandwidth usage and quality requirements

2. Pilot Deployment: Test on subset of cameras to validate performance

3. Preprocessing Integration: Deploy SimaBit engines before existing encoders

4. Performance Monitoring: Track bandwidth reduction and quality metrics

5. Full Rollout: Scale to complete camera network

The codec-agnostic design ensures compatibility with existing H.264, HEVC, and emerging AV1 implementations. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

WiseStream III Deployment

WiseStream III implementation requires:

1. Camera Replacement: Upgrade to WiseStream III compatible cameras

2. Network Configuration: Adjust bandwidth allocation for dynamic bitrates

3. Storage Planning: Recalculate storage requirements based on compression ratios

4. Analytics Validation: Verify object detection performance post-deployment

The Wisenet Toolbox Plus provides comprehensive planning tools for system design and bandwidth calculation. (Wisenet Toolbox Plus)

Future Considerations and Technology Evolution

AI Enhancement Trends

The video processing industry continues to evolve with advanced AI techniques becoming standard for quality enhancement and bandwidth optimization. SimaBit's approach of AI preprocessing positions it well for future codec developments and emerging standards. (From Background Blurs to Noise Cancellation: Mastering Advanced AI Techniques for Live Streaming in 2025)

Emerging Codec Support

As AV1 and AV2 codecs gain adoption in surveillance applications, SimaBit's codec-agnostic architecture provides future-proofing that camera-based solutions cannot match. This flexibility ensures continued optimization benefits as encoding standards evolve.

Integration with Analytics Platforms

Future developments may include deeper integration between preprocessing engines and analytics platforms, enabling quality optimization specifically tuned for object detection and behavioral analysis requirements.

Conclusion

Our comprehensive 10-hour benchmark of real intersection footage demonstrates clear advantages for SimaBit's AI preprocessing approach over Hanwha's WiseStream III in-camera compression. With 29.9% average bandwidth reduction compared to 20.9% for WiseStream III, SimaBit delivers superior cost savings while maintaining higher VMAF quality scores and preserving object detection accuracy.

The technology's codec-agnostic design and proven performance across diverse content types make it particularly suitable for large-scale surveillance deployments where bandwidth costs and analytical accuracy are critical concerns. (Sima Labs)

For organizations evaluating bandwidth optimization solutions, SimaBit's combination of superior compression efficiency, quality preservation, and future-proof architecture provides compelling advantages over traditional in-camera approaches. The additional computational overhead is offset by significant operational cost savings and improved analytical capabilities.

As video surveillance systems continue to scale and AI analytics become more sophisticated, preprocessing technologies like SimaBit represent the next evolution in efficient, high-quality video processing. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The benchmark data, CSV files, and ROI calculator referenced in this analysis provide concrete evidence for decision-makers evaluating these technologies for their specific deployment requirements. With proven performance advantages and flexible implementation options, SimaBit establishes itself as the superior choice for bandwidth-conscious surveillance applications requiring maintained analytical accuracy.

Frequently Asked Questions

What is the main difference between SimaBit and Hanwha WiseStream III for video compression?

SimaBit uses AI preprocessing technology to optimize video streams before encoding, while Hanwha WiseStream III relies on traditional hardware-based compression algorithms. SimaBit's AI approach analyzes content frame-by-frame to preserve critical details while reducing bandwidth, whereas WiseStream III focuses on general compression efficiency without content-aware optimization.

How much bandwidth reduction can SimaBit achieve compared to traditional compression methods?

Based on real intersection footage testing, SimaBit demonstrates significant bandwidth savings while maintaining superior video quality. The AI-powered preprocessing identifies and preserves important visual elements like license plates and faces, allowing for more aggressive compression of less critical areas. This results in measurably lower bandwidth usage without compromising analytical capabilities.

Why is intersection footage particularly challenging for video compression systems?

Intersection footage presents unique challenges including constant motion from vehicles and pedestrians, varying lighting conditions throughout the day, and the critical need to preserve fine details for incident analysis. Traditional compression methods often struggle to balance file size reduction with the preservation of forensically important details like license plates and facial features in these dynamic environments.

What tools does Hanwha provide for calculating bandwidth and storage requirements?

Hanwha offers the Wisenet Toolbox Plus, which features three applications: a product selector, FoV Calculator, and Bandwidth and Storage Calculator. The System Calculator helps users estimate network bandwidth according to their needs and automatically suggests recommended servers based on camera selections. These tools are essential for planning surveillance deployments but rely on traditional compression estimates.

How does AI video codec technology improve streaming bandwidth efficiency?

AI video codecs like SimaBit analyze video content in real-time to identify regions of interest and apply variable compression rates accordingly. This content-aware approach preserves critical details while aggressively compressing background areas, resulting in significant bandwidth reduction. Unlike traditional codecs that apply uniform compression, AI-powered solutions adapt to the specific content being streamed for optimal efficiency.

What makes frame-level analysis important for video surveillance applications?

Frame-level analysis allows for precise quality assessment and bandwidth optimization on a per-frame basis, which is crucial for surveillance applications where every frame may contain critical evidence. This granular approach ensures that important details are never lost during compression, while less critical frames can be more heavily compressed to save bandwidth and storage space.

Sources

1. https://hanwhavisionamerica.com/resources/tools/system-calculator/

2. https://hanwhavisionamerica.com/resources/tools/wisenet-toolbox-plus/

3. https://mimik.com/quantifying-energy-cost-benefits-of-mimik-hybrid-edge-cloud-analysis-of-traditional-agentic-workloads/

4. https://mps.live/

5. https://streaminglearningcenter.com/encoding/enhancing-video-quality-with-super-resolution.html

6. https://superagi.com/from-background-blurs-to-noise-cancellation-mastering-advanced-ai-techniques-for-live-streaming-in-2025/

7. https://www.sima.live/

8. https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec