Results 111 to 120 of about 256 (143)

A contrast sensitivity model of the human visual system in modern conditions for presenting video content. [PDF]

PLoS One
Mozhaeva A, Cree MJ, Durrant RJ, Vlasuyk I, Potashnikov A, Mazin V, Streeter L.   +6 more
europepmc   +1 more source

Analyzing the effect of reasoning-based supervision on face anti-spoofing. [PDF]

Sci Rep
Min J, Lim K, Kim M, Kim D, Oh S, Kim E, Jang H.   +6 more
europepmc   +1 more source

An intelligent object detection and classification framework for assisting visually challenged persons using deep learning and improved crow search optimization. [PDF]

Sci Rep
Khadidos AO, Yafoz A.
europepmc   +1 more source

UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content [PDF]

IEEE Transactions on Image Processing, 2021
IEEE Transactions on Image Processing ...
Alan Bovik, Zhengzhong Tu, Balu Adsumilli   +2 more
exaly   +4 more sources

FAST-VQA: Efficient End-to-End Video Quality Assessment with Fragment Sampling

Lecture Notes in Computer Science, 2022
Current deep video quality assessment (VQA) methods are usually with high computational costs when evaluating high-resolution videos. This cost hinders them from learning better video-quality-related representations via end-to-end training. Existing approaches typically consider naive sampling to reduce the computational cost, such as resizing and ...
Wenxiu Sun, Chen Chaofeng, Weisi Lin
exaly   +3 more sources

LMM-VQA: Advancing Video Quality Assessment With Large Multimodal Models

IEEE Transactions on Circuits and Systems for Video Technology
The explosive growth of videos on streaming media platforms has underscored the urgent need for effective video quality assessment (VQA) algorithms to monitor and perceptually optimize the quality of streaming videos. However, VQA remains an extremely challenging task due to the diverse video content and the complex spatial and temporal distortions ...
Xiongkuo Min, , Guangtao Zhai
exaly   +3 more sources

SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
In recent years, several video quality assessment (VQA) methods have been developed, achieving high performance. However, these methods were not specifically trained for enhanced videos, which limits their ability to predict video quality accurately based on human subjective perception.
Ding-Jiun Huang, Yu-Ting Kao, Tieh-Hung Chuang, Ya-Chun Tsai, Jing-Kai Lou, Shuen-Huei Guan   +5 more
exaly   +3 more sources

Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
Accepted by CVPR 2023 ...
Kai Zhao 0011, Kun Yuan 0003, Ming Sun 0008, Xing Wen   +3 more
exaly   +3 more sources

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3D-DWT cross-band statistics and features for No-Reference Video Quality Assessment (NR-VQA)

Optik, 2021
Abstract This paper presents a robust, novel, and computationally efficient noise estimation-based NR-VQA model. It uses four novel sub-band features; namely cross-band statistics, sub-band kurtosis, sub-band energy ratios, and natural video statistics in three-dimensional discrete wavelet transform (3D-DWT) domain.
Kishor Bhurchandi, Anish Kumar Vishwakarma   +1 more
exaly   +2 more sources