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Found 4 papers, 3 papers with code
Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency
To address this problem, we design a multi-branch deep neural network (DNN) to assess the quality of UHD images from three perspectives: global aesthetic characteristics, local technical distortions, and salient content perception.
SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression
Just noticeable distortion (JND), representing the threshold of distortion in an image that is minimally perceptible to the human visual system (HVS), is crucial for image compression algorithms to achieve a trade-off between transmission bit rate and image quality.
Enhancing Blind Video Quality Assessment with Rich Quality-aware Features
Motivated by previous researches that leverage pre-trained features extracted from various computer vision models as the feature representation for BVQA, we further explore rich quality-aware features from pre-trained blind image quality assessment (BIQA) and BVQA models as auxiliary features to help the BVQA model to handle complex distortions and diverse content of social media videos.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
