Abstract:
Image quality assessment (IQA) has obtained certain achievements with the help of convolutional neural network (CNN). To promote the evaluation performance, most existing...Show MoreMetadata
Abstract:
Image quality assessment (IQA) has obtained certain achievements with the help of convolutional neural network (CNN). To promote the evaluation performance, most existing methods focus on optimizing the structure and parameters of neural networks, while some useful features of image are ignored that can easily be acquired. In this paper, we propose a saliency and error feature fusion IQA (SEFF-IQA) method. Instead of the image itself, two image features, the error between the reference image and the distorted image, and the subjective saliency of distorted image are taken as inputs of the CNN for training. The evaluation score of image quality were obtained by a conventional CNN that trained on frequently used public databases. The proposed method possesses one basic architecture of the CNN only and reduces the volume of training data remarkably compared with state-of-art approaches. Experimental results show that the proposed method is more consistent with human subjective perception than other existing deep learning-based methods.
Date of Conference: 27 May 2022 - 01 June 2022
Date Added to IEEE Xplore: 11 November 2022
ISBN Information: