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HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content

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arxiv 2304.13625 v1 pith:7R2YL37N submitted 2023-04-26 eess.IV cs.CVcs.MM

Rafal K. Mantiuk , Dounia Hammou , Param Hanji This is my paper · ORCID
classification eess.IVcs.CVcs.MM
keywords differencesmetricqualitycontrastdistortionshdr-vdp-3imageprediction
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High-Dynamic-Range Visual-Difference-Predictor version 3, or HDR-VDP-3, is a visual metric that can fulfill several tasks, such as full-reference image/video quality assessment, prediction of visual differences between a pair of images, or prediction of contrast distortions. Here we present a high-level overview of the metric, position it with respect to related work, explain the main differences compared to version 2.2, and describe how the metric was adapted for the HDR Video Quality Measurement Grand Challenge 2023.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding

    eess.IV 2026-07 conditional novelty 7.0 of 10

    AIC2026 is a large-scale benchmark of 9,618 fine-grained distorted images spanning 17 conventional and learned codec configurations across 20 CVVDP-based perceptual levels.

  2. Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions

    cs.CV 2026-07 conditional novelty 7.0 of 10

    A 300+ device crowd-sourced VQA dataset plus Blade-Chest aggregation and a condition-adaptation MLP let standard metrics predict quality orderings under real mobile viewing conditions far better than unadapted baselines.

  3. Single-shot HDR using conventional image sensor shutter functions and optical randomization

    eess.IV 2025-06 accept novelty 7.0 of 10

    Optically shuffling the scene before GRR-shutter capture yields per-pixel random exposures, letting a simple TV-prior inverse problem recover single-shot HDR up to 73 dB from an 8-bit sensor.

  4. Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A new HDR dataset with 100 compressed test images and 34,560 subjective triplet ratings provides JND-scaled quality scores and shows HDR-VDP-2 correlates best with human perception.

  5. Bio-SFT: Asymmetric Cortical Guidance and Retinal Adaptation for Robust HDR Reconstruction

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Bio-SFT, a transformer with retinal adaptation, asymmetric high-to-low frequency guidance, and SNN hard gating, improves HDR-VDP-3 and ΔE_ITP on HDRTV1K with as few as 0.14M parameters.

  6. The JPEG XL Image Coding System: History, Features, Coding Tools, Design Rationale, and Future

    cs.MM 2025-06 conditional novelty 2.0 of 10

    A detailed, author-written companion document to the JPEG XL standard, describing history, every coding tool, design rationale, and self-assessed compression performance claims.

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