Search

Your search keyword '"Bovik, Alan"' showing total 539 results
Start Over Author "Bovik, Alan" Search Limiters Peer Reviewed
539 results on '"Bovik, Alan"'

24. Dual-Stream Complex-Valued Convolutional Network for Authentic Dehazed Image Quality Assessment.

Author

Guan, Tuxin, Li, Chaofeng, Zheng, Yuhui, Wu, Xiaojun, and Bovik, Alan C.

Subjects
CONVOLUTIONAL neural networks, PERCEPTUAL illusions, PERCEPTUAL learning
Abstract

Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

25. One Transform to Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment.

Author

Venkataramanan, Abhinau K., Stejerean, Cosmin, Katsavounidis, Ioannis, and Bovik, Alan C.

Subjects
VIDEO compression, STREAMING media, VIDEOS, MASS media industry, CONTRAST sensitivity (Vision), SOCIAL media
Abstract

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing and by using a transform that is sensitive to visual perception to boost accuracy. Further, we expand the FUNQUE framework to define a collection of improved low-complexity fused-feature models that advance the state-of-the-art of video quality performance with respect to both accuracy, by 4.2% to 5.3%, and computational efficiency, by factors of 3.8 to 11 times! [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

50. Joint source - channel distortion modeling for MPEG-4 video

Author

Sabir, Muhammad Farooq, Heath, Robert W., Jr., and Bovik, Alan Conrad

Subjects
Electric distortion -- Models, Image coding -- Analysis, Wireless networking -- Equipment and supplies, Wireless networking -- Evaluation, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

Catalog

Books, media, physical & digital resources
See catalog results