Video Quality Assessment
94 papers with code • 10 benchmarks • 12 datasets
Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ. SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
Benchmarks
These leaderboards are used to track progress in Video Quality Assessment
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Libraries
Use these libraries to find Video Quality Assessment models and implementationsDatasets
LIVE-VQC
YouTube-UGC
MSU SR-QA Dataset
MSU NR VQA Database
MSU FR VQA Database
LIVE-FB LSVQ
LIVE Livestream
Latest papers with no code
PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition
It is essential to build an effective quality assessment framework to provide a quantifiable evaluation of different images or videos based on the AIGC technologies.
Study of the effect of Sharpness on Blind Video Quality Assessment
A comparative study of the various machine learning parameters such as SRCC and PLCC during the training and testing are presented along with the conclusion.
Perceptual Video Quality Assessment: A Survey
Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display.
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy
Furthermore, a temporal transformer is utilized for spatiotemporal feature fusion across the video.
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models
Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks.
Full-reference Video Quality Assessment for User Generated Content Transcoding
In this work, we observe that existing full-/no-reference quality metrics fail to accurately predict the perceptual quality difference between transcoded UGC content and the corresponding unpristine references.
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception.
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings
In this paper, we propose CLiF-VQA, which considers both features related to human feelings and spatial features of videos.
Geometry-Aware Video Quality Assessment for Dynamic Digital Human
Usually, DDHs are displayed as 2D rendered animation videos and it is natural to adapt video quality assessment (VQA) methods to DDH quality assessment (DDH-QA) tasks.
Video Quality Assessment and Coding Complexity of the Versatile Video Coding Standard
The results consistently demonstrate that VVC outperforms HEVC, achieving bit-rate savings of up to 40% on the subjective quality scale, particularly at realistic bit-rates and quality levels.
