Abstract This work proposes UIL-AQA for long-term Action Quality Assessment AQA designed to be clip-level interpretable and uncertainty-aware. AQA evaluates the execution quality of actions in videos. However, the complexity and diversity of actions, especially in long videos, increase the difficulty of AQA. Existing AQA methods solve this by limiting themselves generally to short-term videos. These approaches lack detailed semantic interpretation for individual clips and fail to account for the impact of human biases and subjectivity in the data during model training. Moreover, although query-based Transformer networks demonstrate strong capabilities in long-term modelling, their interpretability in AQA remains insufficient. This is primarily due to a phenomenon we identified, termed Temporal Skipping , where the model skips self-attention layers to prevent output degradation. We introduce an Attention Loss function and a Query Initialization Module to enhance the modelling capability of query-based Transformer networks. Additionally, we incorporate a Gaussian Noise Injection Module to simulate biases in human scoring, mitigating the influence of uncertainty and improving model reliability. Furthermore, we propose a Difficulty-Quality Regression Module, which decomposes each clip’s action score into independent difficulty and quality components, enabling a more fine-grained and interpretable evaluation. Our extensive quantitative and qualitative analysis demonstrates that our proposed method achieves state-of-the-art performance on three long-term real-world AQA datasets. Our code is available at: https://github.com/dx199771/Interpretability-AQA

Abstract This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments

Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360${^\circ }$video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360${^\circ }$video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360${^\circ }$video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360${^\circ }$sequences. The following factors’ influences were also analyzed: assessment methodology, sequence duration, Head-Mounted Display (HMD) device, uniform and non-uniform coding degradations, and simulator sickness assessment methods. The obtained results have demonstrated the validity of Absolute Category Rating (ACR) and Degradation Category Rating (DCR) for subjective tests with 360${^\circ }$videos, the possibility of using 10-second videos (with or without audio) when addressing quality evaluation of coding artifacts, as well as any commercial HMD (satisfying minimum requirements). Also, more efficient methods than the long Simulator Sickness Questionnaire (SSQ) have been proposed to evaluate related symptoms with 360${^\circ }$videos. These results have been instrumental for the development of the ITU-T Recommendation P.919. Finally, the annotated dataset from the tests is made publicly available for the research community.