Abstract
Image quality assessment (IQA) is a fundamental problem in image processing that aims to measure the objective quality of a distorted image. Traditional full-reference (FR) IQA methods use fixed-size sliding windows to obtain structure information but ignore the variable spatial configuration information. In order to better measure the multi-scale objects, we propose a novel IQA method, named RSEI, based on the perspective of the variable receptive field and information entropy. First, we find that consistence relationship exists between the information fidelity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via rectangular-normalized superpixel segmentation. Then the weights of each image patches are adaptively calculated via their information volume. We verify the effectiveness of RSEI by applying it to data from the TID2008 database and denoise algorithms. Experiments show that RSEI outperforms some state-of-the-art IQA algorithms, including visual information fidelity (VIF) and weighted average deep image quality measure (WaDIQaM).
Highlights
With the rapid development of digital communication, images are playing increasingly important role in modern society
Bosse et al [20] proposed a deep neural networks for image quality assessment (WaDIQaM), and achieved state-of-the-art performance
A shown in Table 1, the last column is the mean opinion scores (MOS) of the images, and the first five columns are the results of Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), visual information fidelity (VIF), feature similarity (FSIM), and rectangle-normalized superpixel entropy index (RSEI), respectively
Summary
With the rapid development of digital communication, images are playing increasingly important role in modern society. Bosse et al [20] proposed a deep neural networks (the network is based on HVS model) for image quality assessment (WaDIQaM), and achieved state-of-the-art performance. Distortions, such as noise and blur, are inevitable in non-ideal image degradation and transmission [21,22]. IQA metrics frequently use fixed-size sliding windows to simulate HVS, such as receptive field [17] They ignore the irregular and inhomogeneous the content and distribution of images, especially the variable spatial configuration information in satellite image. The proposed IQA metric semantically divides the image into multiple flexible patches based on superpixel to accurately measure multi-scale objects in images.
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