Automate BD-PSNR and BD-rate comparisons across encoder configurations.
npx skills add https://github.com/babyworm/rtl-agent-team --skill codec-rd-eval
Please help me install this Agent Skill. Skill: codec-rd-eval Source: https://github.com/babyworm/rtl-agent-team/tree/main/skills/codec-rd-eval Command: npx skills add https://github.com/babyworm/rtl-agent-team --skill codec-rd-eval
Automates the tedious, error-prone process of building reference C encoders, running encoding experiments across multiple sequences and QP points, and producing quantitative rate-distortion comparisons (BD-PSNR / BD-rate) so teams can objectively compare codec algorithm candidates.
Run codec-rd-eval with a completed HJSON test-config to build encoders, execute encodes, compute BD metrics, and generate the RD evaluation report.
High-intent search queries and answers about installing and using this skill.
You can automate BD-rate calculations by running encoding jobs across multiple sequences and QP points using a HJSON configuration, then computing BD-PSNR and BD-rate metrics via VCEG-M33 polynomial fitting to objectively rank N codec candidates.
Yes, rate-distortion evaluation supports parallel encoding orchestration via AWS Batch submission using boto3, allowing large-scale evaluations across configurable sequences and QP points instead of relying solely on local ProcessPoolExecutor.
ffmpeg is an optional dependency required only if you want to opt-in to computing SSIM and VMAF metrics during your rate-distortion comparisons, while numpy and hjson are required for core BD-rate processing.
You can build reference C encoders from a refc/ directory using a reproducible C11 gcc build step, or supply prebuilt encoder binaries, to analyze fixed-point precision impacts and execute design-space exploration studies.
The best way to compare encoder configurations is using VCEG-M33 polynomial fitting with three or more QP points, generating per-sequence and aggregate BD-PSNR or BD-rate metrics rendered into a markdown report.
A HJSON test configuration is required to define custom encoder CLI templates, regex output parsing rules, configurable sequences, and QP points, ensuring reproducible automated encoding and metric computation across candidates.
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