Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Rafal K. Mantiuk , Param Hanji , Maliha Ashraf , Yuta Asano , Alexandre Chapiro This is my paper · ORCID
classification cs.CVcs.GReess.IV
keywords videocolorvideovdpdisplaydistortionsnovelqualitycolorcontrast
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. 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.

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

    cs.CV 2026-07 conditional novelty 5.5 of 10

    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. Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions

    cs.CV 2024-11 conditional novelty 5.0 of 10

    Puzzle Similarity detects artifacts in novel views of 3D scenes by max-pooling feature similarity against training views, and it outperforms prior quality metrics in correlating with human artifact segmentations.

Pith tools