Abstract
Image quality assessment (IQA) is a method to evaluate the perceptual performance of image. Many objective IQA algorithms are developed from the objective comparison of image features, which are mainly trained and evaluated from the ground truth of subjective scores. Due to the inconsistent experiment conditions and cumbersome observing processes of subjective experiments, it is imperative to generate the ground truth for IQA research via objective computation methods. In this paper, we propose a subjective score predictor (SSP) aiming to provide the ground truth of IQA datasets. In perfect accord with distortion information, the distortion strength of distorted image is employed as a dependent parameter. To further be consistent with subjective opinion, on the one hand, the subjective score of source image is viewed as a quality base value, and, on the other hand, we integrate the distortion parameter and the quality base value into a human visual model function to obtain the final SSP value. Experimental results demonstrate the advantages of the proposed SSP in the following aspects: effective performance to reflect the distortion strength, competitive ground truth, and valid evaluation for objective IQA methods as well as subjective scores.
Highlights
Image quality assessment (IQA) is fundamental and important in evaluating and improving the perceptual quality of images, which is widely applied in image-based instrumentation [1, 2]
We propose a subjective score predictor (SSP) aiming to provide the ground truth of IQA datasets
We present the experiments on dataset LIVE II [14] owing to the given distortion parameters, in which 29 high-resolution 24 bits/pixel color images are distorted by five distortion types: JPEG 2000, JPEG, white noise, Gaussian blur, and transmission errors using a fast fading Rayleigh channel model [31]
Summary
Image quality assessment (IQA) is fundamental and important in evaluating and improving the perceptual quality of images, which is widely applied in image-based instrumentation [1, 2]. All objective IQA metrics is to evaluate the image quality in agreement with the subjective opinion of human observers [13], so their performances are validated via comparing with the subjective scores in open IQA datasets such as LIVE [14], A57 [15], CSIQ [16], IVC [17], TID2008 [18], and Toyoma [19]. These datasets only have a limited amount of images since the subjective experiments are time-consuming and expensive [20].
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