Comparing Results: MSU Perceptual Video Quality Tool in Practice

Comparing Results: MSU Perceptual Video Quality Tool in PracticeThe MSU Perceptual Video Quality Tool (PVQT) is a widely used application for objective assessment of video quality. It implements a range of full-reference metrics — where a distorted video is compared against a pristine reference — and is particularly valued in research and engineering workflows for comparing compression algorithms, transmission schemes, denoising methods, and restoration pipelines. This article walks through practical uses of the tool, explains key metrics, outlines a reproducible evaluation workflow, shows how to interpret and compare results, and offers tips to avoid common pitfalls.

What the MSU PVQT does and why it matters

MSU PVQT computes objective video quality metrics that approximate human perception. Using a tool like PVQT helps teams quantify how much quality is lost by an encoder or network impairment, compare candidate algorithms, and tune parameters to balance bitrate and perceived quality.

• Full-reference approach: Requires an uncompressed or high-quality reference video aligned frame-by-frame with the test video.
• Batch processing: Supports running many comparisons automatically and exporting numeric results for further analysis.
• Multiple metrics: Includes classic and perceptually-tuned measures (PSNR, MS-SSIM, VMAF via external integration in some workflows, and MSU’s own perceptual models).

Key metrics provided and what they mean

Below are the most commonly used metrics you’ll see in PVQT and how to interpret them.

• PSNR (Peak Signal-to-Noise Ratio): a simple pixel-wise error measure; higher is better. Useful for coarse comparisons and debugging but poorly correlated with perceived quality in many cases.
• SSIM / MS-SSIM (Structural SIMilarity): evaluates luminance, contrast, and structure; better correlated with perception than PSNR for many distortions.
• MSU PVMetrics (MSU Perceptual models): implementation-specific, designed to model human sensitivity to various distortions. These aim to be more perceptually aligned across complex artifacts.
• Temporal metrics: measures that consider motion and temporal artifacts (flicker, stutter). Important when comparing codecs or network impairments that affect frames differently.
• Bitrate and file-size tradeoffs: while not a perceptual metric, bitrate is essential for plotting rate-distortion (RD) curves (quality vs bitrate).

Practical note: No single metric perfectly matches human opinion. Use multiple complementary metrics and — when possible — a small-scale subjective test to validate conclusions.

Setting up a reproducible evaluation workflow

A consistent evaluation workflow is crucial for meaningful comparisons. Here’s a recommended pipeline:

1. Prepare reference and test videos
   • Ensure same resolution, frame rate, color space, and chroma subsampling.
   • Trim any encoder-introduced delays so frames align exactly.
2. Use a consistent pre-processing pipeline
   • Convert frames to the same pixel format (e.g., YUV420p), color primaries, and transfer characteristics.
   • Avoid color space mismatches — they create large metric differences unrelated to codec quality.
3. Run PVQT in batch mode
   • Group test cases by codec/parameter set.
   • Save CSV or JSON exports with per-frame and aggregate scores.
4. Compute summary statistics
   • Aggregate mean, median, and percentile scores across a dataset.
   • Build RD curves (quality vs bitrate) and BD-rate comparisons.
5. Validate with spot-checks
   • Visually inspect sequences where metrics disagree or where quality differences appear large.

Example: comparing two codecs across a dataset

Suppose you want to compare Codec A and Codec B across 20 test sequences at multiple bitrates. Steps:

• Encode each sequence at target bitrates for both codecs, producing aligned test files.
• Run PVQT to compute PSNR, SSIM/MS-SSIM, and MSU perceptual scores for each file vs reference.
• Export per-sequence CSVs and aggregate into a summary table with mean scores and bitrates.
• Plot RD curves: quality metric on the y-axis, bitrate on the x-axis (log scale often helpful).
• Compute BD-rate to estimate average bitrate savings at equivalent quality.

Interpreting results:

• If Codec A yields consistently higher MSU perceptual scores at the same bitrate, it likely produces better perceived quality.
• If PSNR favors one codec but MSU perceptual or MS-SSIM favors the other, prefer the perceptual metric for viewer-oriented decisions.
• Investigate sequences with high variance — they reveal content types where codecs perform differently (fast motion, fine textures, synthetic content).

Visualizing and reporting results

Good visualizations make conclusions clear:

• RD curves for each codec across multiple sequences and an average curve.
• Bar charts showing mean metric differences and confidence intervals.
• Heatmaps of per-sequence wins/losses (which codec was better for each metric).
• Scatter plots of bitrate vs metric showing per-file points.

Include sample frames where codecs diverge. Side-by-side frame crops or short GIFs help stakeholders see the perceptual differences that metrics summarize.

Common pitfalls and how to avoid them

• Mismatched color spaces or pixel formats: convert both reference and test to a common format before measurement.
• Ignoring alignment: frame shifts produce huge errors — verify timestamps and trim as needed.
• Over-reliance on PSNR: it’s easy to optimize for PSNR at the expense of perceptual quality. Use perceptual metrics.
• Small or biased test sets: include diverse content (motion, textures, dark scenes, cartoons) to get robust results.
• Mixing resolutions/frame rates: compare like with like or resample consistently.

When to run subjective tests

Objective metrics are proxies. Run subjective tests when:

• Small metric differences have business impact (e.g., claiming a new codec is perceptually better).
• Introducing a new perceptual optimization whose effects are unclear.
• Validating a new metric for your content type.

Run a controlled subjective test (DSCQS, ACR, or pairwise comparison) with enough observers and randomized presentation to get reliable MOS (Mean Opinion Score) data.

Quick checklist before publishing results

• Confirm reference/test alignment and formats.
• Use multiple metrics (include at least one perceptual metric).
• Aggregate across a representative content set and report spread (std/percentiles).
• Visualize RD curves and include sample frames for qualitative context.
• Disclose processing steps (color conversions, filters, cropping).

Conclusion

MSU Perceptual Video Quality Tool is a practical and powerful tool for objective VQA in research and engineering workflows. Its value increases when used within a rigorous, reproducible pipeline, combined with complementary perceptual metrics and selective subjective validation. Proper setup, diverse content, and careful interpretation of multiple metrics are the keys to meaningful comparisons.