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Common Pitfalls When Using PSNR and How to Avoid Them

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Common Pitfalls When Using PSNR and How to Avoid ThemPeak Signal-to-Noise Ratio (PSNR) is one of the most widely used objective metrics for measuring reconstruction quality in images and video. It’s simple to compute and often correlates with perceived quality for some tasks, which explains its popularity in research papers, codec evaluations, and engineering workflows. However, PSNR has important limitations and can be misused in ways that produce misleading conclusions. This article explains common pitfalls when using PSNR and gives practical recommendations to avoid them.

1) Treating PSNR as a universal measure of perceptual quality

PSNR is derived from mean squared error (MSE) and quantifies pixel-wise differences. It does not model human visual perception, contrast sensitivity, structural masking, color processing, or the importance of edges and textures.

  • Why it’s a problem:

    • Two images with similar PSNR can have very different perceptual quality.
    • Artifacts that are perceptually obvious (blocking, ringing, blurring of edges) can yield modest PSNR changes.
    • Some distortions (e.g., small geometric shifts, tone mapping) lower PSNR significantly while being imperceptible or less objectionable to viewers.

  • How to avoid:

    • Use perceptual metrics alongside PSNR, e.g., SSIM, MS-SSIM, VMAF, or modern learning-based metrics (e.g., LPIPS).
    • Validate algorithmic improvements with human subjective tests when feasible (e.g., MOS, pairwise comparison).
    • When publishing results, report multiple metrics and include example images/videos showing the visual differences.

2) Comparing PSNR across different resolutions, color spaces, or dynamic ranges

PSNR depends on the dynamic range and scaling of pixel values and is not directly comparable across datasets with different bit depths, color encodings, or preprocessing steps.

  • Why it’s a problem:

    • 8-bit vs. 10-bit video: the same absolute MSE implies different perceptual significance.
    • Linear RGB vs. gamma-corrected (sRGB) or YCbCr: errors distribute differently across channels; simply computing PSNR on RGB may misrepresent perceived error.
    • HDR content has larger numerical ranges; PSNR values will differ from SDR even for similar perceived quality.

  • How to avoid:

    • Always state bit depth, color space, and whether calculations were done on linear or gamma-corrected data.
    • For video, compute PSNR in a luminance channel (Y) using a defined colorspace conversion (e.g., ITU-R BT.709 or BT.2020) when comparing codecs, since luminance differences matter more perceptually.
    • Normalize or scale data consistently before computing PSNR. For HDR work, use HDR-aware metrics or convert to a perceptual space (e.g., PQ or HLG) before evaluation.

3) Using different PSNR definitions or implementations without consistency

There are subtle differences in how PSNR is implemented: per-channel vs. overall, frame-averaged vs. global, use of Y-only PSNR, and whether border pixels or chroma subsampling are handled.

  • Why it’s a problem:

    • Inconsistent definitions lead to non-reproducible comparisons and apparent improvements that are implementation artifacts.
    • Some tools compute PSNR per-channel then average; others compute overall MSE across channels.

  • How to avoid:

    • Define your PSNR computation precisely in papers, reports, or experiments: specify per-channel or Y-only, how channels are weighted (if at all), frame averaging method, color conversion matrices, and any cropping or border handling.
    • Use well-known reference implementations (e.g., FFmpeg’s psnr filter with documented options) and report versions and command lines.
    • When comparing to prior work, match their PSNR computation settings or re-run their method with your PSNR setup.

4) Ignoring spatial or temporal pooling strategies

PSNR is often reported as an average over frames or images. How you pool frame-level PSNR into a single score can change conclusions, especially with variable scene complexity or transient artifacts.

  • Why it’s a problem:

    • Averaging PSNR across frames weights each frame equally, but some frames (with high motion or complexity) may be more important perceptually.
    • Peak artifacts in a few frames can be diluted by averaging, masking occasional but severe failures.

  • How to avoid:

    • Report distributional statistics in addition to mean PSNR: median, standard deviation, min/max, and percentiles (e.g., 5th percentile) to show worst-case behavior.
    • For video, consider weighted pooling that accounts for temporal masking or saliency, or use perceptual video metrics like VMAF that include pooling strategies.
    • Provide per-frame plots or sample problematic frames when evaluating algorithms.

5) Overfitting to PSNR during model training or codec tuning

When researchers optimize models or compressors to maximize PSNR exclusively, they may produce artifacts that “game” MSE-based metrics but are visually poor (e.g., over-smoothing, color shifts that reduce squared error).

  • Why it’s a problem:

    • Models trained solely with MSE/PSNR objectives tend to produce blurred results since averaging multiple plausible outputs minimizes MSE.
    • Tuning encoder parameters to maximize PSNR may sacrifice aspects like texture and naturalness that humans prefer.

  • How to avoid:

    • Use perceptual loss terms (SSIM-based, adversarial, feature-space losses like VGG perceptual loss) alongside MSE during training.
    • Evaluate on perceptual metrics and human tests, not only PSNR.
    • Regularly inspect qualitative outputs (zoomed-in patches, textures, motion sequences) during development.

6) Computing PSNR after lossy pre/post-processing or misaligned frames

Small misalignments (subpixel shifts) or different cropping/scale at the decoder vs. reference can cause large PSNR drops unrelated to reconstruction quality. Similarly, denoising, histogram matching, or different gamma handling will alter PSNR.

  • Why it’s a problem:

    • Motion-compensated codecs or scaling filters may introduce alignments different from reference, making pixel-wise MSE meaningless.
    • Preprocessing (e.g., denoising) on reference or distorted images can bias PSNR.

  • How to avoid:

    • Ensure spatial alignment: apply identical cropping, resizing, and color conversion to reference and test images before computing PSNR.
    • If subpixel motion or registration is suspected, perform motion-compensated comparison or use perceptual metrics robust to small shifts (e.g., MS-SSIM, LPIPS).
    • Explicitly document any pre/post-processing and include the exact commands or code used.

7) Applying PSNR to tasks it wasn’t designed for

PSNR is a general-purpose pixel-wise discrepancy measure; it’s not appropriate for tasks where structural fidelity, semantics, or high-level perception matter more than exact pixel values, such as super-resolution, style transfer, inpainting, or generative synthesis.

  • Why it’s a problem:

    • Super-resolved images that look sharp and natural can have lower PSNR than overly smooth outputs that are numerically closer to the ground truth.
    • For generative models, multiple plausible outputs exist; PSNR unfairly penalizes any output that differs from the single ground-truth sample.

  • How to avoid:

    • Use task-appropriate metrics: perceptual metrics for super-resolution, FID/IS for generative quality (with caution), or task-specific measures (e.g., recognition accuracy for downstream vision tasks).
    • Combine objective metrics with human evaluation when perceptual quality is the goal.

8) Misinterpreting small PSNR differences as significant

Because PSNR is on a logarithmic scale, small numerical differences may or may not be meaningful depending on dataset size and variance.

  • Why it’s a problem:

    • A 0.1–0.5 dB PSNR increase is often within measurement noise and not necessarily perceptible.
    • Statistical significance is rarely assessed; small mean differences across many frames can be driven by outliers.

  • How to avoid:

    • Report confidence intervals and perform statistical tests (paired t-test, Wilcoxon signed-rank) when comparing methods.
    • Use large, diverse test sets and report effect sizes alongside p-values.
    • Complement PSNR differences with visual examples and perceptual metrics.

9) Failing to account for channel weighting and chroma subsampling

When working with YCbCr and chroma-subsampled video (e.g., 4:2:0), how you compute and weight chroma and luma errors affects PSNR.

  • Why it’s a problem:

    • Treating chroma channels the same as luma can over- or under-emphasize chroma errors relative to perceived quality.
    • Many codecs operate in subsampled chroma; naive upsampling or channel handling can introduce artifacts that skew PSNR.

  • How to avoid:

    • Compute PSNR primarily on the luma (Y) channel for perceptual relevance, and report chroma PSNR separately if needed.
    • When including chroma, state the weighting used or compute a weighted PSNR consistent with perceptual channel importance.
    • Use correct upsampling filters when converting subsampled chroma to full resolution before PSNR.

10) Using PSNR without clear reproducibility (missing metadata)

A PSNR number without context (dataset, bit depth, color space, crop, tool versions) is of limited use.

  • Why it’s a problem:

    • Readers can’t assess fairness or reproduce results.
    • Small differences in conversion matrices, gamma handling, or per-frame alignment change PSNR by tenths of dB.

  • How to avoid:

    • Publish exact evaluation protocol: tools and versions, command lines, color conversion matrices, bit depth, scaling, cropping, and dataset descriptions.
    • Share code or scripts used to compute PSNR and any preprocessing steps.
    • When comparing to prior art, attempt to re-run baselines with your evaluation pipeline or clearly note differences.

Practical checklist before reporting PSNR

  • Specify bit depth and color space.
  • State whether PSNR is computed on Y, RGB, or per-channel.
  • Document any cropping, resizing, or registration performed.
  • Report mean, median, std, min/max, and percentiles for frame-level PSNR.
  • Include at least one perceptual metric (SSIM/VMAF/LPIPS) and, if possible, subjective ratings.
  • Provide exact commands/code and tool versions for reproducibility.
  • Perform statistical tests to confirm significance of differences.

Conclusion

PSNR remains useful as a simple, objective indicator of pixel-wise fidelity and for quick diagnostics. But treating it as a definitive measure of visual quality or using it inconsistently leads to misleading claims. Combine PSNR with perceptual metrics and human evaluation, be explicit about implementation details, and use robust pooling and statistical analysis. Doing so will yield evaluations that are fairer, reproducible, and better aligned with human perception.

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