# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 1 Introduction  
Visual perception stands as one of the most crucial senses for humans. Images, serving as an efficient and practical carrier of visual information, are progressively taking on a paramount role in human life. However, the processes of image acquisition, transmission, and storage are subject to various distortions, such as Gaussian white noise, motion blur, and white balance errors [1]. Perceptual image quality assessment (IQA) can be dichotomized into subjective and objective evaluations. The former is constrained by environmental factors and exhibits suboptimal practicality. Conversely, objective IQA predominantly leverages algorithmic methods to predict image quality, with the goal of attaining a high degree of congruence between computed values and human visual perception, ultimately supplanting human vision in the task of image quality assessment. In recent years, as the depth of research continues to expand, the theoretical underpinning of objective IQA research has gradually matured. Generally, objective IQA can be categorized into three types based on the availability of reference images [2]: full reference (FR) [3], reduced reference (RR) [4], and no reference (NR) [5] IQA. The primary focus of this paper lies in the investigation of FR-IQA methods. Current research on FR-IQA methods is relatively advanced, and depending on their computational approaches, can be broadly classified into three categories: mathematical methods, methods predicated on human vision system (HVS) characteristics, and learning-based methods [6]. Mathematical methods apply direct computations on image pixel gray values, but they overlook the varying contributions of different pixels to human vision. This oversight leads to discrepancies between computed results and subjective perception [7]. In recent years, researchers have started to incorporate HVS characteristics [8]--including brightness adaptation, contrast sensitivity, masking effects, and attention mechanisms--into IQA research. A seminal method in this domain is the structural similarity index (SSIM) proposed by [PERSON] [9], which assesses image quality via the image's brightness, contrast, and structural information using the RMS algorithm for contrast computation [10]. This method has a high computational efficiency, but the computational accuracy needs to be obviously improved. Several researchers have enhanced the SSIM algorithm. For instance, [PERSON] [11] proposed the three-component weighted SSIM (3-SSIM) method, which calculates feature similarities for the edge, texture, and smooth areas across the entire image. Although the calculation accuracy of SSIM has been further improved by 3-SSIM, the generalization cannot be ideal. [PERSON] [12] introduced contrast and visual saliency similarity (CVSS), which amalgamates image contrast with visual saliency. [PERSON] [13] proposed the gradient similarity metric (GSM) index by masking the structural information. Although the aforementioned methods exhibit high computational accuracy, their generalizability need to be optimized. From an information theory standpoint, [PERSON] [14] proposed visual information fidelity (VIF), which boasts a high computational efficiency but exhibits limited generalizability and calculation accuracy. The Laplacian of Gaussian (LoG) operator excels in simulating human visual perception characteristics and is frequently employed in the computer vision-related literature [15]. [PERSON] [16] proposed the feature similarity index (FSIMc) using LoG wavelets to calculate phase consistency. [PERSON] [17] presented the visual saliency-induced index (VSI) for perceptual image quality assessment, drawing upon the variable attention of the human eye to different scenes. VSI employs LoG filters to extract image frequency domain features and applies image gradient and chromaticity information to the method. [PERSON] [18] proposed a perceptual similarity index (PerSIM) method using LoG on the luminance channel to mimic the visual system on the human retina.