Dynamic Receptive Field Generation for Full-Reference Image Quality Assessment

Woojae Kim, Anh Duc Nguyen, Sanghoon Lee, Alan Conrad Bovik

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8970465
Pages (from-to)4219-4231
Number of pages13
JournalIEEE Transactions on Image Processing
Volume29
DOIs
Publication statusPublished - 2020

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint

Dive into the research topics of 'Dynamic Receptive Field Generation for Full-Reference Image Quality Assessment'. Together they form a unique fingerprint.

Cite this