ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing

Super-Resolution (SR) is a critical task in computer vision, focusing on reconstructing high-resolution (HR) images from low-resolution (LR) inputs. The field has seen significant progress through various challenges, particularly in single-image SR. Video Super-Resolution (VSR) extends this to the temporal domain, aiming to enhance video quality using methods like local, uni-, bi-directional propagation, or traditional upscaling followed by restoration. This challenge addresses VSR for conferencing, where LR videos are encoded with H.265 at fixed QPs. The goal is to upscale videos by a specific factor, providing HR outputs with enhanced perceptual quality under a low-delay scenario using causal models. The challenge included three tracks: general-purpose videos, talking head videos, and screen content videos, with separate datasets provided by the organizers for training, validation, and testing. We open-sourced a new screen content dataset for the SR task in this challenge. Submissions were evaluated through subjective tests using a crowdsourced implementation of the ITU-T Rec P.910.

Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs

Machine learning-based video codecs have made significant progress in the past few years. A critical area in the development of ML-based video codecs is an accurate evaluation metric that does not require an expensive and slow subjective test. We show that existing evaluation metrics that were designed and trained on DSP-based video codecs are not highly correlated to subjective opinion when used with ML video codecs due to the video artifacts being quite different between ML and video codecs. We provide a new dataset of ML video codec videos that have been accurately labeled for quality. We also propose a new full reference video quality assessment (FRVQA) model that achieves a Pearson Correlation Coefficient (PCC) of 0.99 and a Spearman's Rank Correlation Coefficient (SRCC) of 0.99 at the model level. We make the dataset and FRVQA model open source to help accelerate research in ML video codecs, and so that others can further improve the FRVQA model.