research-article

Precise No-Reference Image Quality Evaluation Based on Distortion Identification

Authors:

Yongbing Zhang,

Xiangyang JiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 3s

Article No.: 110, Pages 1 - 21

https://doi.org/10.1145/3468872

Published: 15 November 2021 Publication History

Abstract

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.

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Index Terms

Precise No-Reference Image Quality Evaluation Based on Distortion Identification
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 3s

October 2021

324 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3492435

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Publication History

Published: 15 November 2021

Accepted: 01 May 2021

Revised: 01 May 2021

Received: 01 November 2020

Published in TOMM Volume 17, Issue 3s

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Funding Sources

National Key Research and Development Program of China
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Zhejiang Province Natural Science Foundation of China
111 Project
Shenzhen Science and Technology Project
Guangdong Special Support

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