Blind image quality prediction by exploiting multi-level deep representations. (September 2018)

• Record Type: Journal Article
• Authors: Gao, Fei; Yu, Jun; Zhu, Suguo; Huang, Qingming; Tian, Qi
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 432
• Page End: 442
• Publication Date: 2018-09
• Subjects: Image quality assessment -- Deep learning -- Convolutional Neural Networks (CNN) -- Multi-level deep representation -- Support vector regression; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.04.016
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We leverage the benefits introduced by very deep DNNs and the difficulty in training a very deep DNN model. We reason the image quality at each level of representation, to get the best of both the intermediate-level and high-level representations. The proposed method works remarkably well and is highly comparable to state-of-the-art BIQA methods, over various canonical datasets. Abstract: Blind image quality assessment (BIQA) aims at precisely estimating human perceived image quality with no access to a reference. Recently, several attempts have been made to develop BIQA methods based on deep neural networks (DNNs). Although these methods obtained promising performance, they have some limitations: (1) their DNN models are actually "shallow" in term of depth; and (2) these methods typically use the output of the last layer in the DNN model as the feature representation for quality prediction. Since the representation depth has been demonstrated beneficial for various vision tasks, it is significant to explore very deep networks for learning BIQA models. Besides, the information in the last layer may unduly generalize over local artifacts which are highly related to quality degradation. On the contrary, intermediate layers may be sensitive to local degradations but will not capture high-level semantics. Thus, reasoning at multiple levels of representation is necessary in the IQA task. In this paper, we propose to extract multi-level representations from a very deep DNN model for learning an effective BIQA model, and consequently present a simple but extraordinarily effective BIQA framework, codenamed BLINDER ( BLind Image quality predictioN via multi-level DEep Representations ). Thorough experiments have been conducted on five standard databases, which show that a significant improvement can be achieved by adopting multi-level deep representations. Besides, BLINDER considerably outperforms previous state-of-the-art BIQA methods for authentically distorted images.