Most full-reference image quality assessment (FR-IQA) methods advanced to date have been holistically designed without regard to the type of distortion impairing the image. However, the perception of distortion depends nonlinearly on the distortion type. Here we propose a novel FR-IQA framework that dynamically generates receptive fields responsive to distortion type. Our proposed method- dynamic receptive field generation based image quality assessor (DRF-IQA)-separates the process of FR-IQA into two streams: 1) dynamic error representation and 2) visual sensitivity-based quality pooling. The first stream generates dynamic receptive fields on the input distorted image, implemented by a trained convolutional neural network (CNN), then the generated receptive field profiles are convolved with the distorted and reference images, and differenced to produce spatial error maps. In the second stream, a visual sensitivity map is generated. The visual sensitivity map is used to weight the spatial error map. The experimental results show that the proposed model achieves state-of-the-art prediction accuracy on various open IQA databases.
|Number of pages||13|
|Journal||IEEE Transactions on Image Processing|
|Publication status||Published - 2020|
Bibliographical noteFunding Information:
Manuscript received May 21, 2019; revised November 18, 2019; accepted January 11, 2020. Date of publication January 27, 2020; date of current version February 7, 2020. This work was supported by the Samsung Research Funding Center of Samsung Electronics under Project SRFC-IT1702-08. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Marta Mrak. (Corresponding author: Sanghoon Lee.) Woojae Kim, Anh-Duc Nguyen, and Sanghoon Lee are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: email@example.com; firstname.lastname@example.org; email@example.com).
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All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design