# Ubuntu/Debiansudo apt updatesudo apt install ffmpeg libvmaf-dev# Verify VMAF supportffmpeg -hide_banner -filters | grep vmaf

#!/bin/bash# baseline_encode.shINPUT_FILE=$1OUTPUT_DIR=$2BITRATE=$3ffmpeg -i "$INPUT_FILE" \  -c:v libx264 \  -preset medium \  -b:v "${BITRATE}k" \  -maxrate "$((BITRATE * 12 / 10))k" \  -bufsize "$((BITRATE * 2))k" \  -g 60 \  -keyint_min 60 \  -sc_threshold 0 \  "$OUTPUT_DIR/baseline_${BITRATE}k.mp4"

#!/bin/bash# Run baseline encoding for all test clipsTEST_CLIPS=("ElFuente_4096x2160_60fps_10bit_420.y4m"             "Meridian_4096x2160_60fps_10bit_420.y4m"             "Chimera_4096x2160_60fps_10bit_420.y4m")BITRATES=(6000 4000 2500 1500 800)for clip in "${TEST_CLIPS[@]}"; do  for bitrate in "${BITRATES[@]}"; do    echo "Encoding $clip at ${bitrate}kbps..."    ./baseline_encode.sh "$clip" "baseline_results" "$bitrate"  donedone

#!/bin/bash# vmaf_assessment.shSOURCE_FILE=$1ENCODED_FILE=$2OUTPUT_LOG=$3ffmpeg -i "$ENCODED_FILE" -i "$SOURCE_FILE" \  -lavfi "[0:v]scale=1920:1080:flags=bicubic[scaled]; \          [scaled][1:v]vmaf=log_path=$OUTPUT_LOG:log_fmt=json" \  -f null

{  "preprocessing": {    "noise_reduction": {      "enabled": true,      "strength": "medium"    },    "detail_enhancement": {      "enabled": true,      "adaptive": true    },    "temporal_consistency": {      "enabled": true,      "lookahead_frames": 5    }  },  "output": {    "format": "yuv420p",    "bit_depth": 8  }}

import simabit# Initialize preprocessing engineengine = simabit.PreprocessingEngine(config_path="config.json")# Process video fileinput_video = "source_content.mp4"processed_video = "preprocessed_content.yuv"engine.process_video(input_video, processed_video)# Continue with existing encoding pipeline# ffmpeg -i preprocessed_content.yuv [encoding_params] output.mp4

# Submit processing jobcurl -X POST https://api.sima.live/v1/preprocess \  -H "Authorization: Bearer YOUR_API_KEY" \  -H "Content-Type: application/json" \  -d '{    "input_url": "https://your-storage.com/source.mp4",    "output_url": "https://your-storage.com/processed.yuv",    "config": {      "noise_reduction": {"strength": "medium"},      "detail_enhancement": {"adaptive": true}    }  }'

# Direct FFmpeg integrationffmpeg -i input.mp4 \  -vf "simabit=config=config.json" \  -f yuv4mpeg - | \ffmpeg -f yuv4mpeg -i - \  -c:v libx264 \  -preset medium \  -b:v 4000k \  output.mp4

#!/bin/bash# automated_quality_test.shTEST_CONTENT_DIR="test_clips"RESULTS_DIR="quality_results"BITRATES=(6000 4000 2500 1500 800)mkdir -p "$RESULTS_DIR/baseline" "$RESULTS_DIR/simabit"for clip in "$TEST_CONTENT_DIR"/*.mp4; do  clip_name=$(basename "$clip" .mp4)    # Process with SimaBit  simabit_process "$clip" "$RESULTS_DIR/simabit/${clip_name}_processed.yuv"    for bitrate in "${BITRATES[@]}"; do    # Baseline encoding    ffmpeg -i "$clip" \      -c:v libx264 -preset medium -b:v "${bitrate}k" \      "$RESULTS_DIR/baseline/${clip_name}_${bitrate}k.mp4"        # SimaBit + encoding    ffmpeg -i "$RESULTS_DIR/simabit/${clip_name}_processed.yuv" \      -c:v libx264 -preset medium -b:v "${bitrate}k" \      "$RESULTS_DIR/simabit/${clip_name}_${bitrate}k.mp4"        # VMAF assessment    assess_vmaf "$clip" \      "$RESULTS_DIR/baseline/${clip_name}_${bitrate}k.mp4" \      "$RESULTS_DIR/baseline_vmaf_${clip_name}_${bitrate}k.json"        assess_vmaf "$clip" \      "$RESULTS_DIR/simabit/${clip_name}_${bitrate}k.mp4" \      "$RESULTS_DIR/simabit_vmaf_${clip_name}_${bitrate}k.json"  donedone

import jsonimport pandas as pdimport matplotlib.pyplot as pltdef analyze_vmaf_results(baseline_dir, simabit_dir):    results = []        for vmaf_file in os.listdir(baseline_dir):        if vmaf_file.endswith('.json'):            # Parse baseline VMAF            with open(os.path.join(baseline_dir, vmaf_file)) as f:                baseline_data = json.load(f)            baseline_vmaf = baseline_data['pooled_metrics']['vmaf']['mean']                        # Parse SimaBit VMAF            simabit_file = vmaf_file.replace('baseline_', 'simabit_')            with open(os.path.join(simabit_dir, simabit_file)) as f:                simabit_data = json.load(f)            simabit_vmaf = simabit_data['pooled_metrics']['vmaf']['mean']                        # Extract metadata            clip_name = vmaf_file.split('_')[2]            bitrate = int(vmaf_file.split('_')[3].replace('k.json', ''))                        results.append({                'clip': clip_name,                'bitrate': bitrate,                'baseline_vmaf': baseline_vmaf,                'simabit_vmaf': simabit_vmaf,                'improvement': simabit_vmaf - baseline_vmaf            })        return pd.DataFrame(results)# Generate quality improvement reportdf = analyze_vmaf_results('baseline_vmaf', 'simabit_vmaf')print(f"Average VMAF improvement: {df['improvement'].mean():.2f} points")print(f"Quality improvement at 4000kbps: {df[df['bitrate']==4000]['improvement'].mean():.2f} points")

def calculate_bitrate_reduction(df):    reductions = []        for clip in df['clip'].unique():        clip_data = df[df['clip'] == clip].sort_values('bitrate')                for _, simabit_row in clip_data.iterrows():            simabit_vmaf = simabit_row['simabit_vmaf']            simabit_bitrate = simabit_row['bitrate']                        # Find baseline bitrate that achieves same VMAF            baseline_data = clip_data[clip_data['baseline_vmaf'] >= simabit_vmaf]            if not baseline_data.empty:                baseline_bitrate = baseline_data.iloc[0]['bitrate']                reduction = (baseline_bitrate - simabit_bitrate) / baseline_bitrate * 100                                reductions.append({                    'clip': clip,                    'simabit_bitrate': simabit_bitrate,                    'equivalent_baseline_bitrate': baseline_bitrate,                    'reduction_percent': reduction                })        return pd.DataFrame(reductions)reduction_df = calculate_bitrate_reduction(df)print(f"Average bitrate reduction: {reduction_df['reduction_percent'].mean():.1f}%")

def calculate_baseline_costs(monthly_gb, cost_per_gb):    """Calculate baseline CDN costs"""    return monthly_gb * cost_per_gbdef calculate_tiered_costs(monthly_gb):    """Calculate costs using tiered pricing"""    tiers = [        (10 * 1024, 0.085),    # First 10 TB        (40 * 1024, 0.080),    # Next 40 TB          (100 * 1024, 0.060),   # Next 100 TB        (350 * 1024, 0.040),   # Next 350 TB        (float('inf'), 0.020)  # Remaining    ]        total_cost = 0    remaining_gb = monthly_gb        for tier_limit, rate in tiers:        if remaining_gb <= 0:            break                    tier_usage = min(remaining_gb, tier_limit)        total_cost += tier_usage * rate        remaining_gb -= tier_usage        return total_cost# Example calculationmonthly_bandwidth_gb = 150 * 1024  # 150 TBbaseline_cost = calculate_tiered_costs(monthly_bandwidth_gb)print(f"Baseline monthly CDN cost: ${baseline_cost:,.2f}")

def calculate_simabit_savings(baseline_cost, bandwidth_reduction_percent):    """Calculate monthly savings from bandwidth reduction"""    reduced_bandwidth = monthly_bandwidth_gb * (1 - bandwidth_reduction_percent / 100)    reduced_cost = calculate_tiered_costs(reduced_bandwidth)    monthly_savings = baseline_cost - reduced_cost    annual_savings = monthly_savings * 12        return {        'monthly_savings': monthly_savings,        'annual_savings': annual_savings,        'reduction_percent': bandwidth_reduction_percent,        'roi_percent': (annual_savings / (monthly_savings * 0.1)) * 100  # Assuming 10% of monthly savings as implementation cost    }# Calculate with 22% bandwidth reductionsavings = calculate_simabit_savings(baseline_cost, 22)print(f"Monthly savings: ${savings['monthly_savings']:,.2f}")print(f"Annual savin## Frequently Asked Questions### How does SimaBit reduce bandwidth by 22% without changing my H.264 encoder?SimaBit uses AI-powered video optimization technology that works as a post-processing layer after your existing H.264 encoder. It analyzes the encoded video stream and applies intelligent compression techniques to reduce bandwidth requirements while maintaining visual quality. This approach allows you to keep your current encoding infrastructure intact while achieving significant bandwidth savings.### What are the main benefits of using SimaBit for live streaming?SimaBit offers multiple benefits including 22% or more bandwidth reduction, lower CDN costs, reduced buffering for viewers, and improved Quality of Experience (QoE). Unlike codec upgrades that require infrastructure overhauls, SimaBit integrates seamlessly with existing H.264 workflows, providing immediate cost savings without the complexity of encoder replacements or months of testing.### How does AI video codec technology like SimaBit compare to traditional encoding optimization?AI video codec technology leverages machine learning algorithms to optimize video streams in ways traditional encoders cannot. While traditional optimization focuses on encoder parameter tuning, AI-based solutions like SimaBit analyze content patterns and apply intelligent compression that adapts to different video types. This results in superior bandwidth reduction while maintaining or even improving visual quality compared to standard encoding approaches.### Is SimaBit integration complex and will it affect my streaming latency?SimaBit is designed for easy integration with minimal impact on streaming workflows. The integration process is straightforward and doesn't require replacing existing encoders or extensive infrastructure changes. The AI optimization is highly efficient and adds minimal processing overhead, ensuring that streaming latency remains within acceptable bounds for live applications.### Can SimaBit work with other video optimization techniques like per-title encoding?Yes, SimaBit can complement other optimization techniques including per-title encoding. While per-title encoding optimizes ABR ladder renditions and bitrates for specific content, SimaBit provides an additional layer of AI-powered compression that can further reduce bandwidth requirements. This combination can lead to even greater cost savings and improved streaming performance.### What kind of cost savings can I expect from implementing SimaBit?With a 22% bandwidth reduction, you can expect proportional savings in CDN costs, storage requirements, and egress fees. For high-traffic streaming services, this translates to significant monthly savings without any quality compromise. The exact savings depend on your current streaming volume, but the reduction in data transfer costs alone often justifies the implementation within the first few months.## Sources1. [https://arxiv.org/abs/2508.06753](https://arxiv.org/abs/2508.06753)2. [https://bitmovin.com/blog/per-title-encoding-savings/](https://bitmovin.com/blog/per-title-encoding-savings/)3. [https://www.sima.live/blog/boost-video-quality-before-compression](https://www.sima.live/blog/boost-video-quality-before-compression)4. [https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec](https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec)