Blind Image Quality Assessment of Natural Scenes Based on Entropy Differences in the DCT Domain

Abstract

Blind/no-reference image quality assessment is performed to accurately evaluate the perceptual quality of a distorted image without prior information from a reference image. In this paper, an effective blind image quality assessment approach based on entropy differences in the discrete cosine transform domain for natural images is proposed. Information entropy is an effective measure of the amount of information in an image. We find the discrete cosine transform coefficient distribution of distorted natural images shows a pulse-shape phenomenon, which directly affects the differences of entropy. Then, a Weibull model is used to fit the distributions of natural and distorted images. This is because the Weibull model sufficiently approximates the pulse-shape phenomenon as well as the sharp-peak and heavy-tail phenomena of natural scene statistics rules. Four features that are related to entropy differences and human visual system are extracted from the Weibull model for three scaling images. Image quality is assessed by the support vector regression method based on the extracted features. This blind Weibull statistics algorithm is thoroughly evaluated using three widely used databases: LIVE, TID2008, and CSIQ. The experimental results show that the performance of the proposed blind Weibull statistics method is highly consistent with that of human visual perception and greater than that of the state-of-the-art blind and full-reference image quality assessment methods in most cases.

Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels

“…Another is the KonIQ-10k database [49], which is an IQA dataset to date consisting of 10,073 quality scored images. Through the use of crowdsourcing, researchers obtained 1.2 million reliable quality ratings from 1459 crowd workers, with the score range of [1,5], and the better the quality is, the higher the value is.…”

“…In comparison, the performance of the EPL method based on the most proper amount of initial training data is compared with the most advanced NR-IQA methods, including: classical NR-IQA methods (BLIINDSS [30], BRISQUE [28], BWS [5], CORNIA [31], GMLOG [51], IL-NIQE [6], and FRIQUEE [34]), and DNN-based NR-IQA methods (CNN [12], RankIQA [23], BIECON [20], DIQaM [17], DIQA [22], CaHFI [52], NRVPD [53], ESD [54], VS-DDON [55], NQS-GAN [56], and ILGNet [57]). This method was also compared with the well-known DNN models, AlexNet [10], ResNet50 [48], and VGG-16 [26], which were modeled using the LIVEC database.…”

“…Some NR-IQA methods have been based on a specific distortion [3,4], which commonly used the prior knowledge of the distortion type. To assess the image quality with no information available, natural scene statistics (NSS)-based methods have been widely used to extract reliable features, which assume the natural images share certain statistics and the occurrence of distortions can change these statistics [5][6][7][8][9]. Nevertheless, the hand-crafted features have always been designed for a specific type of distortion which lies in the low-level feature and leads to insufficient feature extraction and analysis.…”

Current Trends and Advances in Image Quality Assessment

“…Nevertheless, some of no-reference metrics, e.g. NIQE, BLIINDS, DIIVINE or BRISQUE [54], utilize also natural scene statistics, anisotropy [55], [56] or entropy [57], [58].…”

A Comparative Study of DNN-Based Models for Blind Image Quality Prediction

“…In each database, we randomly select 80% of the distorted images as the training set and the remaining 20% of images as the testing set. There is no overlap in image contents between these training and test- We compare the state-of-the-art BIQA and FR-IQA methods, including: FR-IQA methods (PSNR, SSIM [3], FSIMc [36] ) and classic BIQA methods (BRISQUE [37], COR-NIA [38], GMLOG [39], ILNIQE [40] and BWS [43]), current leading BIQA methods based on DNN (MGDNN [12], FRIQUEE [11], DLIQA [19], SESANIN [21], BLNDER [15], CNN [22], RANKIQA [25], DIQaM [28], BIECON [29], DIQA [30] , DB-CNN [41], HIQA [42]). However, it is difficult to exactly reproduce the IQA methods based on DNN.…”