HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos
Joshua P. Ebenezer, Zaixi Shang, Yongjun Wu, Hai Wei, Sriram, Sethuraman, Alan C. Bovik

TL;DR
HDR-ChipQA is a no-reference video quality assessment model specifically designed for HDR videos, emphasizing distortions at luminance extremes to improve accuracy over existing SDR-based models.
Contribution
The paper introduces a novel nonlinear preprocessing step that enhances distortion detection at luminance extremes, improving HDR video quality assessment accuracy.
Findings
Outperforms existing SDR VQA algorithms on HDR content
Achieves state-of-the-art performance on SDR videos
Enhances distortion sensitivity at luminance extremes
Abstract
We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in massively scaled video networks has driven the need for video quality assessment (VQA) algorithms that better account for distortions on HDR content. In particular, standard VQA models may fail to capture conspicuous distortions at the extreme ends of the dynamic range, because the features that drive them may be dominated by distortions {that pervade the mid-ranges of the signal}. We introduce a new approach whereby a local expansive nonlinearity emphasizes distortions occurring at the higher and lower ends of the {local} luma range, allowing for the definition of additional quality-aware…
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Taxonomy
TopicsImage Enhancement Techniques · Image and Video Quality Assessment · Advanced Image Processing Techniques
Methodsfail
