PREDICTION OF CHROMATIC VISUAL MASKING WITH DEEP LEARNING

被引：0

作者：

Chetouani, Aladine ^{[1
]}

Pedersen, Marius ^{[2
]}

Moan, Steven Le ^{[3
]}

机构：

[1] Univ Orleans, PRISME Lab, Orleans, France

[2] Norwegian Univ Sci & Technol, Dept Comp Sci, Trondheim, Norway

[3] Massey Univ, Dept Mech & Elect Engn, Palmerston North, New Zealand

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2020年

关键词：

Visual Masking; Image Quality; Deep Learning;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

Visual masking is a well-studied phenomenon that has been exploited for signal compression, computer graphics and data hiding. Among the different types of visual masking, chromatic masking has received very little attention despite its importance and proven potential for the aforementioned applications. In this paper, we ask whether a deep neural network can learn to predict the detection thresholds in a chromatic masking paradigm. For that, a CNN model was trained and evaluated using a dataset made of 480 image patches for which chromatic thresholds were registered in terms of log-Gabor targets, as well as Root Mean Square (RMS) error. Experimental results show the superiority of the proposed approach.

引用

页码：146 / 150

页数：5

共 23 条

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