codec-rd-eval

Build and evaluate ref_model C encoder binaries across multiple encoding configurations, computing BD-PSNR and BD-rate metrics (VCEG-M33 methodology) to quantitatively compare codec algorithm candidates.

This skill automates the full Rate-Distortion evaluation pipeline:

1. Build encoder binaries from refc/*.c (C11, gcc)
2. Run parallel encoding simulations across (sequence, QP, config) combinations
3. Compute BD-PSNR/BD-rate using VCEG-M33 polynomial interpolation (3+ points)
4. Generate comparison reports with per-sequence and aggregate metrics

Scope: Encoder RD evaluation only. This skill evaluates encoder quality metrics (BD-PSNR, BD-rate, optional SSIM/VMAF). For decoder conformance testing against JVET/JCTVC bitstreams, use /rtl-agent-team:codec-conformance-eval.

Phase-agnostic: While commonly used during rat-dse Step 3b, this skill can be invoked at any Phase where quantitative RD comparison of encoder configurations is needed — Phase 1 (algorithm exploration), Phase 2 (architecture validation), Phase 4 (fixed-point precision impact), or standalone evaluation outside the pipeline.

Execution modes:

• local: ProcessPoolExecutor-based parallel encoding on local CPU cores
• aws-batch: Optional AWS Batch spot instance submission for large-scale evaluation

Key features:

• N-candidate comparison: Compare 2+ configurations via candidates[] array (anchor + N tests)
• Configurable encoder CLI: encoder_cmd_template supports any encoder (HM, VTM, custom). Available variables: {encoder}, {cfg}, {input}, {width}, {height}, {fps}, {frames}, {qp}, {bitstream}, {recon}, {bit_depth}, {chroma_format}
• Configurable output parsing: Custom regex patterns for bitrate/PSNR extraction
• SSIM/VMAF opt-in: Additional quality metrics on explicit user request only
• bit_depth/chroma_format aware: YUV weighting adjusts per chroma format (420/422/444)
• 3+ QP point support: Standard 4-point (exact fit), 5+ (least-squares), 3 (quadratic fallback)

<Use_When>

• Comparing algorithm candidates with objective quality metrics (any Phase)
• Measuring BD-PSNR/BD-rate between anchor and modified encoder configurations
• Evaluating fixed-point precision impact on codec quality (e.g., 12-bit vs 16-bit paths)
• Validating that HW-friendly algorithm modifications preserve acceptable quality
• N-way comparison of multiple encoder configurations (candidates[] mode)
• The user explicitly says "RD eval", "BD-PSNR", "BD-rate", "codec quality", "algorithm quality evaluation" </Use_When>

<Do_Not_Use_When>

• No ref C model encoder exists yet (build ref model first via ref-model skill)
• Comparing RTL vs C model output (use /rtl-agent-team:rtl-model-consistency instead)
• Running decoder conformance tests against standard bitstreams (use /rtl-agent-team:codec-conformance-eval)
• Running RTL-level conformance against reference decoder (use /rtl-agent-team:rtl-conformance-test)
• Non-codec designs where RD metrics don't apply </Do_Not_Use_When>

<Why_This_Exists> In codec design, algorithm selection has the highest impact on final quality and area. Theoretical complexity analysis (operations/pixel, gate estimates) provides useful guidance but cannot capture the full picture — actual RD performance on representative sequences is the definitive metric.

BD-PSNR/BD-rate (VCEG-M33) is the universally accepted method in the video coding community for comparing codec configurations. It normalizes across different operating points (QP values) to produce a single, meaningful comparison metric.

Without this skill, teams either skip quantitative RD evaluation (risking suboptimal algorithm selection) or manually set up evaluation infrastructure (time-consuming and error-prone). </Why_This_Exists>

<Execution_Policy>

• Requires refc/*.c to exist (or user-specified encoder source path)
• HJSON test configuration defines all evaluation parameters
• Local execution is the default; AWS Batch is opt-in via configuration
• Simulation results are cached at .rat/scratch/rd-eval/ for re-analysis
• Report is generated at the path specified in test configuration (default: docs/phase-1-research/rd-eval-report.md)
• On build failure: report error details and stop (do not proceed with stale binaries)
• On simulation failure: report failed jobs, compute BD metrics from successful jobs with warnings
• On metric parsing failure (bitrate=0 or PSNR=0): mark job as failed with guidance to check output_parsing
• timeout_per_job is in seconds (default: 3600s = 1 hour per encoding job)
• SSIM/VMAF are computed ONLY when explicitly requested via quality_metrics config
• Dependencies: gcc (C11), Python 3.9+, numpy, hjson. Optional: ffmpeg (required for SSIM/VMAF computation when quality_metrics includes "ssim" or "vmaf"), boto3 (required only for aws-batch execution mode)
• Self-test: python3 skills/codec-rd-eval/scripts/bd_rate.py --test runs built-in unit tests </Execution_Policy>

1. **Prerequisite validation** - Verify refc/*.c (or configured encoder_src) exists - Verify test configuration file exists (HJSON format) - If not provided, generate from template at skills/codec-rd-eval/templates/test-config.hjson - Ask user to fill in sequence paths and encoder configurations - Check anchor encoder binary existence (skip build if already built) - Verify Python dependencies: `python3 -c "import numpy; import hjson"` - If missing, report: `pip install numpy hjson`

2. Encoder build (build_encoder.sh)

   • For each unique encoder_src in configurations (anchor/test or candidates[]): bash skills/codec-rd-eval/scripts/build_encoder.sh <src> <binary> [extra_cflags]
   • Build flags: gcc -std=c11 -O2 -Wall -Wextra -lm (C11 standard per CLAUDE.md)
   • On build failure: capture stderr, report to user, STOP

3. Simulation execution (run_eval.py)

   • Parse HJSON test configuration
   • Generate job matrix: (sequence x QP x config) combinations
   • Execute in configured mode:
     • local: python3 skills/codec-rd-eval/scripts/run_eval.py <config.hjson> --mode local
     • aws-batch: python3 skills/codec-rd-eval/scripts/run_eval.py <config.hjson> --mode aws-batch
   • Each job produces: bitrate_kbps, psnr_y/u/v/yuv (dB), encode_time_s
   • Optional: SSIM, VMAF (when quality_metrics includes them)
   • Results saved to: .rat/scratch/rd-eval/results.json

4. BD-PSNR/BD-rate calculation (bd_rate.py)

   • python3 skills/codec-rd-eval/scripts/bd_rate.py .rat/scratch/rd-eval/results.json --output .rat/scratch/rd-eval/bd-metrics.json
   • VCEG-M33 algorithm with N-point support:
     1. Transform rates to log10 domain
     2. Fit polynomial (degree = min(3, N-1)) — exact interpolation for 4 points, least-squares fitting for 5+
     3. Integrate over common PSNR range
     4. BD-rate (%) and BD-PSNR (dB)
   • N-candidate mode: compute metrics for each test vs anchor
   • Per-sequence results + aggregate average + encoding time summary
   • Output: .rat/scratch/rd-eval/bd-metrics.json

5. Report generation

   • Generate report from template at skills/codec-rd-eval/templates/rd-eval-report.md
   • Output path: as configured in HJSON (default: docs/phase-1-research/rd-eval-report.md)
   • Report contains:
     • Evaluation summary (anchor label, test label, date)
     • Per-sequence RD data table (QP, bitrate, PSNR, encode time)
     • Per-sequence BD-PSNR and BD-rate
     • Aggregate BD-PSNR and BD-rate
     • Encoding time comparison table
     • SSIM/VMAF tables (if opt-in metrics enabled)
     • N-candidate comparison matrix (if candidates[] mode)
     • Interpretation guidance
   • If invoked from rat-dse: feed BD metrics back to algorithm comparison matrix

<Tool_Usage>

# ============================================================
# Step 1: Prerequisite validation
# ============================================================
Glob("refc/*.c")                       # Verify encoder source exists
Read("<test-config.hjson>")            # Read test configuration
Bash("python3 -c 'import numpy; import hjson; print(\"OK\")'")  # Check dependencies

# ============================================================
# Step 2: Encoder build (for each unique encoder_src)
# ============================================================
Bash("bash skills/codec-rd-eval/scripts/build_encoder.sh refc .rat/scratch/rd-eval/anchor_encoder")
Bash("bash skills/codec-rd-eval/scripts/build_encoder.sh refc .rat/scratch/rd-eval/test_encoder")

# ============================================================
# Step 3: Simulation execution (parallel)
# ============================================================
Bash("python3 skills/codec-rd-eval/scripts/run_eval.py <config.hjson> --mode local",
     timeout=600000)  # Up to 10 min for large evaluations

# ============================================================
# Step 4: BD metric calculation
# ============================================================
Bash("python3 skills/codec-rd-eval/scripts/bd_rate.py .rat/scratch/rd-eval/results.json --output .rat/scratch/rd-eval/bd-metrics.json")

# ============================================================
# Step 5: Report generation
# ============================================================
# Read bd-metrics.json and generate markdown report
Read(".rat/scratch/rd-eval/bd-metrics.json")
# Write report to configured output path

</Tool_Usage>

**Example 1: Algorithm comparison during DSE** ``` User: "Compare RD performance of 3 H.264 intra prediction algorithm candidates" → Invoke /rtl-agent-team:codec-rd-eval → Step 1: Verify refc/ exists, generate test-config.hjson (candidates[] mode) → Step 2: Build 3 encoders: SAD, Hadamard, SATD+RDOQ → Step 3: Simulate BasketballDrill, BQTerrace, RaceHorses × QP{22,27,32,37} → Step 4: vs SAD(anchor): Hadamard BD-rate=-3.2%, SATD+RDOQ BD-rate=-5.1% → Step 5: Generate docs/phase-1-research/rd-eval-report.md (with N-candidate matrix) → "SATD+RDOQ achieves best BD-rate -5.1% over SAD. Hadamard gives -3.2%." ```

Example 2: Fixed-point precision evaluation with SSIM

User: "Measure quality difference between 12-bit vs 16-bit internal paths including SSIM"
→ quality_metrics: ["psnr", "ssim"] configured
→ anchor: 16-bit internal path encoder
→ test: 12-bit internal path encoder
→ BD-PSNR = -0.02 dB, BD-rate = +0.5%, SSIM delta = -0.0001
→ "12-bit path shows BD-rate +0.5% vs 16-bit, negligible SSIM difference (-0.0001). Recommend 12-bit considering gate count savings."

Example 3: No encoder source available

User: "Run BD-rate comparison"
→ Step 1: refc/*.c not found
→ "No ref C model encoder source found. Run /rtl-agent-team:ref-model first to generate the reference model."

<Escalation_And_Stop_Conditions>

• refc/ does not exist → suggest running ref-model skill first
• Encoder build fails → report gcc error details, check C11 compliance
• Test sequences not found at configured paths → ask user for correct paths
• All simulations fail → check encoder binary, report common error pattern
• Metric parsing returns 0 → suggest configuring output_parsing patterns in HJSON
• BD-rate shows unexpected large degradation (>20%) → warn user, suggest verifying encoder correctness
• AWS Batch credentials not configured → fall back to local mode with warning
• numpy/hjson not installed → provide pip install command
• SSIM/VMAF requested but ffmpeg not available → warn and skip optional metrics </Escalation_And_Stop_Conditions>

<Final_Checklist> Before reporting completion, verify ALL of the following:

• Encoder binary built successfully (exit code 0)
• All (or majority of) simulation jobs completed successfully
• No parsing failures (bitrate=0 or PSNR=0 on successful encodes)
• BD-PSNR and BD-rate calculated for each sequence
• Aggregate BD metrics computed
• Encoding time comparison included in report
• Report generated at configured output path
• Raw data preserved at .rat/scratch/rd-eval/
• If N-candidate mode: comparison matrix generated
• If SSIM/VMAF requested: optional metrics included
• If invoked from rat-dse: BD metrics available for algorithm comparison matrix

If ANY item is unchecked → DO NOT report completion. Fix the issue first. </Final_Checklist>