Bootstrapping HDR video quality analysis from SDR via data-adaptive grading

Abstract

High Dynamic Range (HDR) video in Ultra-High Definition (UHD) has been in use now for a number of years, in streaming services like Netflix and Amazon Prime and even YouTube, as well as in broadcast, on blu-ray, and can even be captured by consumers using high-end cameras and drones. These applications typically use HDR video with wide color gamut (BT.2020 color space) compressed in the H.265/HEVC video codec. While HDR video is thus by now ubiquitous, measuring the quality of HDR video objectively remains challenging. First, the peak-signal-to-noise ratio (PSNR), never a high-quality measure, becomes even less reliable in HDR. Moreover, with the wide color gamut typically used, one would expect color space distortion measures to play an essential role; several have been tested in the literature, but none has proven reliable. Finally, while there exists both visual quality databases for HDR images, and HDR image quality measures, there is virtually no widely available database for HDR video (we understand that there are some in development), nor a single, widely recognized HDR video quality measure. On the other hand, the field of video quality analysis is by now well-established for standard dynamic range (SDR) video, and FastVDO has developed such a measure (FastVDO Quality, FVQ). In this paper, we propose to make progress in HDR Video Quality Analysis (VQA) by initiating a novel bootstrapping method to measure the quality of HDR video by developing a dynamic conversion of HDR to standard dynamic range (SDR), and combining the video quality measure of the SDR using the FVQ measure previously developed, and a measure for the surplus quality of the HDR over the SDR video. The central conversion function is itself based on an approach previously developed by FastVDO and described in standard documents and presented at SPIE.