https://github.com/k-zha14/zoom-vqa
공식 코드Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment
Papers with Code 아카이브
Video Quality Assessment
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kalofan/autoscanpathqa
공식 코드Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
https://github.com/lidq92/LinearityIQA
공식 코드Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
https://github.com/lidq92/MDTVSFA
공식 코드Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training
https://github.com/lidq92/VSFA
공식 코드https://github.com/lighting-yxli/tinq
공식 코드TINQ: Temporal Inconsistency Guided Blind Video Quality Assessment
https://github.com/limuhit/vr-video-quality-in-the-wild
공식 코드Perceptual Quality Assessment of Virtual Reality Videos in the Wild
https://github.com/littlespray/crave
공식 코드Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency
https://github.com/littlespray/e-bench
공식 코드VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
https://github.com/littlespray/ve-bench
공식 코드VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
https://github.com/lixinustc/kvq-challenge-cvpr-ntire2024
공식 코드NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
https://github.com/lixinustc/kvqe-challenge-cvpr-ntire2025
공식 코드NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
https://github.com/ljh0v0/fmd-frechet-motion-distance
공식 코드Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
https://github.com/lskdream/gamival
공식 코드GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
https://github.com/microsoft/p.910
공식 코드https://github.com/msu-video-group/erqa
공식 코드ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
https://github.com/msu-video-group/msu_vqm_compression_benchmark
공식 코드Video compression dataset and benchmark of learning-based video-quality metrics
https://github.com/mv-lab/videoai-speedrun
공식 코드AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
https://github.com/pavancm/GREED
공식 코드https://github.com/pavancm/contrique
공식 코드https://github.com/pavancm/conviqt
공식 코드https://github.com/prashnani/PerceptualImageError
공식 코드PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
https://github.com/q-future/q-align
공식 코드Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
https://github.com/q-future/visual-question-answering-for-video-quality-assessment
공식 코드VQA$^2$: Visual Question Answering for Video Quality Assessment
https://github.com/qmme/stablevqa
공식 코드StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability
https://github.com/qmme/t2vqa
공식 코드Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment
https://github.com/qualityassessment/bvqi
공식 코드Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
https://github.com/qyp2000/kvq
공식 코드KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception
https://github.com/richzhang/PerceptualSimilarity
공식 코드The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/shankhanil006/ssl-vqa
공식 코드Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos
https://github.com/shankhanil006/vision
공식 코드https://github.com/sissuire/sama
공식 코드Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment
https://github.com/sunwei925/CompressedVQA
공식 코드https://github.com/sunwei925/SimpleVQA
공식 코드A Deep Learning based No-reference Quality Assessment Model for UGC Videos
https://github.com/sunwei925/dn-piqa
공식 코드https://github.com/sunwei925/minimalisticvqa
공식 코드Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.