.png){kind=icon}
返回
Pre-Attention and Spatial Dependency Driven No-Reference Image Quality Assessment
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2019.2900941.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The excessive emulation of the human visual system and the lack of connection between chromatic data and distortion have been the major bottlenecks in developing image quality assessment. To address this issue, we develop a new no-reference (NR) image quality assessment (IQA) metric that accounts for the impact of pre-attention and spatial dependency on the perceived quality of distorted images. The resulting model, dubbed the Pre-attention and Spatial-dependency driven Quality Assessment (PSQA) predictor, introduces the pre-attention theory to emulate early phase visual perception by refining luminance-channel data. Chromatic data are also processed concurrently by transforming images from RGB to the perceptually optimized SCIELAB color space. Considering that the gray-tone spatial dependency matrix conveys important texture properties that are closely related to visual quality, this matrix, as a mathematical solution for subsequent visual process emulation, is calculated along with its statistical features on both gray and color channels. To clarify the influence of different regression procedures on model output, support vector regression and AdaBoosting Back Propagation (BP) neural networks are adopted separately to train the prediction models. We thoroughly evaluated PSQA on four public image quality databases: LIVE, TID2013, CSIQ, and VCL. The experimental results show that PSQA delivers highly competitive performance compared with top-rank NR and full-reference IQA metrics.
Keyword:
Pre-attention
no-reference (NR)
image quality assessment (IQA)
spatial dependency
chromatic data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.7
论文数:
4.7K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
查看更多内容