Deep CNN-Based Blind Image Quality Predictor

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 1; pp. 11 - 24
• Main Authors: Kim, Jongyoo, Nguyen, Anh-Duc, Lee, Sanghoon
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Computer vision; Convolutional neural network (CNN); deep learning; Distortion; Error analysis; Image processing; Image quality; image quality assessment (IQA); Machine learning; Mathematical models; Neural networks; no-reference IQA (NR-IQA); Object recognition; Quality assessment; Quality control; Reliability; State of the art; Training; Visual system; Visualization
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2829819

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.