# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 1 Introduction
The three aforementioned methods all utilize the LoG filter in the process of establishing IQA methods, and their evaluation results exhibit commendable accuracy. However, these IQA methods still have some deficiencies in generalization. Learning-based methods [19] typically necessitate feature extraction and method training. However, due to limited datasets, a protracted method training process, and a dependence on data information, these methods bear a significant disadvantage in terms of generalizability. [PERSON] [20] employed the VGGnet (ConvNets trained for image recognition) to construct IQA methods by the improved architecture of deep neural networks. [PERSON] [21] used a convolutional neural network to generate a visual difference map mapping, and weighted the visual difference map according to the local spatial characteristics of the distorted image to evaluate the image quality. [PERSON] [22] used several difference of Gaussian (DoG) frequency bands to extract image features, and construct IQA methods through random forest regression. Learning-based methods are also used in the field of NR-IQA, for example, [PERSON] et al. employed a neural network-based pairwise learning-to-rank algorithm named RankNet [23] to learn the IQA method by incorporating the uncertainty into the loss function. [PERSON] [24] proposed a two-stage NR-IQA method based on CNN. This method uses the local quality score generated by the full-reference image quality assessment as the image patch score label. With the rapid development of IQA, IQA-related researches have been widely used in the fields of face recognition and biometric classification in recent years [25; 26; 27]. FR-IQA methods are designed to serve human vision, and an ideal computational method should mirror human visual perception in its representation of image texture and structural content. In general, the chromaticity channels of an image also contain structural information, but the way in which to extract this information needs to be explored. In the previous research, LoG filtering was utilized to extract structural information on the image brightness channel. Based on this method, more extensive structural information can also be obtained by LoG filtering processing on luminance channel and chromaticity channels, respectively. In an RGB image, the brightness channel and chromaticity channels are mixed. Hence, it is difficult to extract more extensive structural information. In order to solve this problem, the RGB image can be transformed to a uniform color space. LMN is the widely used uniform color space, and it has been utilized in some IQA research [1; 17]. In this article, the image structure information is extracted on the three channels of L, M, and N by LoG filtering. Then, the three filtering components are fused by mathematical formula. This calculation process can be termed as multi-channel LoG filtering (MLG). Since alterations in contrast impact the HVS perception of image quality [28] and the contrast of the filtered image remains consistent, this paper introduces image contrast to offset the aforementioned limitations. The proposed method primarily targets color images, and this paper aims to conduct rudimentary chromaticity similarity calculations on the chromaticity channels [29]. After the completion of the calculations for the three components, the final quality score is computed using the standard deviation (SD)-based [28] pooling summation method. The main contribution of this paper is to propose an IQA method that combines the three image characteristics, e.g., multi-channel structural information by LoG filtering, and contrast and chroma information. Finally, a stable and efficient FR-IQA MVFC is established. The superiority of the proposed method will be substantiated from multiple perspectives in the succeeding sections.