Call for Paper & Model

Objective Video Quality Models (VQM) have been an active research area for decades, aiming to automate the video quality assessment tasks and processes. Even with the well-known deficiency of the existing methods (PSNR, SSIM, VMAF, etc.), most OTT (over the top) streaming service providers are leveraging objective VQM to improve their video encoding efficiency, and monitor and control the video quality along the streaming workflow. Therefore, continuous improvement of the objective VQM models in terms of their accuracy (i.e. correlations with human perceptual quality) and runtime performance will enable the OTT service providers to drive down their encoding costs while maintaining a high-quality experience for streaming customers.

Over the years, HDR video contents have seen increasing adoption by various streaming and video hosting services (such as Amazon Prime Video, Netflix, and YouTube). HDR is also increasingly available as part of live broadcast and streaming workflows. Streaming HDR contents has introduced unique challenges related to the quality of user experience and the performance of video compression algorithms. The increases in bit depth and the use of nonlinear transfer functions in HDR can change the visibility and severity of compression distortions. Being able to objectively measure and control HDR perceptual quality has become a critical capability needed for premium video streaming services. However, the lack of generalizable VQM models that works on both HDR and SDR has become a bottleneck for OTT service providers to scale up their offerings and make improvement to the compression efficiency and perceptual quality for HDR contents.

In this grand challenge, we invite the research community to participate and submit novel/improved VQM models for objectively predicting HDR and SDR video quality for both full-reference and no-reference use cases. HDR & SDR video dataset with human subjective quality scores (as ground truth) will be shared to facilitate the VQM model training and testing. The new dataset is collected using pairwise comparison (PC) instead of Absolute Category Rating (ACR) protocol for the reduced uncertainty. The challenge will focus on predicting video quality of both HDR and SDR videos with various degrees of compression and scaling artifacts. 

The submitted HDR-capable VQM models could generalize well to SDR (standard dynamic range) videos, so that a single VQM can reliably measure the objective video quality for both SDR and HDR video contents.