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ColorVideoVDP: A visual difference predictor for image, video and display distortions

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arxiv 2401.11485 v2 pith:RM7HKTKR submitted 2024-01-21 cs.CV cs.GReess.IV

ColorVideoVDP: A visual difference predictor for image, video and display distortions

Rafal K. Mantiuk , Param Hanji , Maliha Ashraf , Yuta Asano , Alexandre Chapiro This is my paper
classification cs.CV cs.GReess.IV
keywords videocolorvideovdpdisplaydistortionsnovelqualitycolorcontrast
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision, for both luminance and color. The metric is built on novel psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video streaming distortions (e.g. video compression, rescaling, and transmission errors), and also 8 new distortion types related to AR/VR displays (e.g. light source and waveguide non-uniformities). To address the latter application, we collected our novel XR-Display-Artifact-Video quality dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on XR-DAVID, as well as several datasets from the literature, indicate a significant gain in prediction performance compared to existing metrics. ColorVideoVDP opens the doors to many novel applications which require the joint automated spatiotemporal assessment of luminance and color distortions, including video streaming, display specification and design, visual comparison of results, and perceptually-guided quality optimization.

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Cited by 3 Pith papers

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

    cs.CV 2026-07 conditional novelty 7.0

    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.

  2. Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation

    cs.CV 2026-07 conditional novelty 5.5

    A hybrid DSCS+PC light-field QA framework and public dataset show objective metrics drop when view-synthesis/3DGS distortions join coding artifacts, and view pooling affects agreement.

  3. Dynamic XR Rendering Offloading Based on Feature-Based Quality Assessment

    eess.IV 2026-06 unverdicted novelty 4.0

    The paper presents an edge-aided XR testbed with dynamic offloading, a deep feature-based perceptual quality metric robust to misalignments, and a contextual bandit controller for real-time rendering decisions.