# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 1 Introduction  
As the crucial aspect in optimization problems of image processing applications, the image quality assessment (IQA) algorithms aim to automatically and accurately evaluate the quality of a given image without accessing the ground truth [1; 2; 3; 4; 5]. Compared with full reference (FR) [6; 7] IQA and reduced reference (RR) IQA [8] algorithms, blind IQA (BIQA) algorithms can estimate the perceptual quality of a distorted image without using any information of its pristine image. Therefore, BIQA algorithms are more valuable in practice. Early BIQA algorithms mainly focus on evaluating the perceptual quality of images that are corrupted by specific distortions, and assume that the distortion type is known beforehand, such as blur distortion [9], JPEG compression [10] and ringing distortion [11]. Although these algorithms have achieved satisfying results, they are limited to certain types of distortions in practice. By contrast, the general purpose BIQA algorithms do not require knowing the distortion types, which makes them much more practical and can be applied in various occasions. Generally speaking, the general purpose BIQA algorithms usually share a similar architecture, i.e., quality-aware feature extraction and quality pooling, and the performance of a BIQA algorithm is more dependent on quality-aware feature extraction. The most widely used approach to quality-aware feature extraction is based on the natural scene statistics (NSS) [12], which are altered in the presence of distortions. For example, [PERSON] and [PERSON] proposed a distortion identification-based image verity and integrity evaluation (DIIVINE) algorithm that extracts a series of statistical features based on NSS by integrating the wavelet transform and Gabor transform [12]. In [13], [PERSON] et al. described a block-based discrete cosine transform domain NSS feature extraction approach to BIQA algorithm (named BLIINDS-II). [PERSON] et al. [14] proposed a complex extension of the DIIVINE algorithm (C-DIIVINE) utilizing complex statistical features with both magnitude and phase information on wavelet domain to assess image quality. Although aforementioned algorithms can achieve satisfactory prediction performance, they are not suitable for the real-world applications to some extent due to the time-consuming multiple sub-bands wavelet transformation and block-based feature extraction process. To provide an efficient way to tackle the above problem, BRISQUE [15] preprocessed an image by local mean removal transformation to generate mean subtracted contrast normalized coefficient map, and employed the generalized Gaussian distribution (GGD) and asymmetric GGD (AGGD) models to extract the distribution features. Due to their low computational cost and good performance, quality-aware features used in BRISQUE have also been adopted by many other BIQA algorithms [16; 17; 18]. For example, Natural Image Quality Evaluator (NIQE) [16] collects statistical features to build an NSS-based prediction model without training the model on human-rated distorted images. [PERSON] et al. [17] extended NIQE algorithm (dubbed ILNIQE) to capture local distortion artifacts more comprehensively by integrating the features of luminance, color, gradient, and Log-Gabor filter responses. However, the performances of NIQE and ILNIQE are inferior to the state-of-the-art BIQA algorithms. To improve the prediction accuracy and assess image quality for authentically distorted images, [PERSON] and [PERSON] [18] proposed FRIQUEE to extract more complex statistical features from various types of transformed maps including luminance map, sigma map, difference of Gaussian of sigma map, Laplacian of the luminance map, subbands of a complex steerable pyramid, and feature maps from CIELAB, LMS, RGB, and HSI color spaces. Despite the great improvement achieved by FRIQUEE, its real-time implementation is still a challenging task due to the time-consuming feature extraction process. There are still others algorithms dedicated to using other kinds of perceptual features.