Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method - deep image quality assessor (DIQA) - separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
|Number of pages||14|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - 2019 Jan|
Bibliographical noteFunding Information:
Manuscript received March 1, 2017; revised October 22, 2017 and March 23, 2018; accepted April 9, 2018. Date of publication June 12, 2018; date of current version December 19, 2018. This work was supported by the National Research Foundation of Korea through the Korea Government (MSIT) under Grant 2016R1A2B2014525. (Corresponding author: Sanghoon Lee.) The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: firstname.lastname@example.org; email@example.com).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence