Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 1, Pages 11-24Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2829819
Keywords
Convolutional neural network (CNN); deep learning; image quality assessment (IQA); no-reference IQA (NR-IQA)
Categories
Funding
- National Research Foundation of Korea through the Korea Government (MSIT) [2016R1A2B2014525]
Ask authors/readers for more resources
Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the stateof- the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to noreference 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available