Utilizing Image Scales Towards Totally Training Free Blind Image Quality Assessment

Abstract

A new approach to blind image quality assessment (BIQA), requiring no training, is proposed in this paper. The approach is named as blind image quality evaluator based on scales and works by evaluating the global difference of the query image analyzed at different scales with the query image at original resolution. The approach is based on the ability of the natural images to exhibit redundant information over various scales. A distorted image is considered as a deviation from the natural image and bereft of the redundancy present in the original image. The similarity of the original resolution image with its down-scaled version will decrease more when the image is distorted more. Therefore, the dissimilarities of an image with its low-resolution versions are cumulated in the proposed method. We dissolve the query image into its scale-space and measure the global dissimilarity with the co-occurrence histograms of the original and its scaled images. These scaled images are the low pass versions of the original image. The dissimilarity, called low pass error, is calculated by comparing the low pass versions across scales with the original image. The high pass versions of the image in different scales are obtained by Wavelet decomposition and their dissimilarity from the original image is also calculated. This dissimilarity, called high pass error, is computed with the variance and gradient histograms and weighted by the contrast sensitivity function to make it perceptually effective. These two kinds of dissimilarities are combined together to derive the quality score of the query image. This method requires absolutely no training with the distorted image, pristine images, or subjective human scores to predict the perceptual quality but uses the intrinsic global change of the query image across scales. The performance of the proposed method is evaluated across six publicly available databases and found to be competitive with the state-of-the-art techniques.

Blind image quality assessment based on aesthetic and statistical quality-aware features

“…Saha and Wu 18 classified BIQA methods into two main categories: distortion specific and general purpose (or universal) as shown in Fig. 2.…”

Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment

“…Furthermore, a natural image might have objects and structures that are relevant at different scales, but the human eye is readily able to identify and process the information presented by it [28]. Thus, processing an image at various scales adds flexibility to the processing technique, and image scales play a very important role in IQA [4], [29]. In [4], five scales, namely, the original image scale plus the other four reduced resolution (each down sampled by a factor of 2), were utilized to design the multi-scale SSIM.…”

Incremental Compression for Image Database

“…Incremental image of the third compression methods are pseudo video sequence compression method, also known as the method based on graph theory, more representative of the method is [4][5][6][7][8][9][10]. In these methods, firstly, a minimum spanning tree is established, which makes the weights and the minimum of all edges.…”

“…This method only uses the information of spatial redundancy in the image frame, without making full use of the inter frame redundancy, the compression effect can not meet the needs of the data explosion era. In order to use inter frame redundancy compression encoding method based on graph theory to generate pseudo video sequences is proposed in [4][5][6][7][8][9][10], but this method in the face image database incremental image, incremental image usually has little or no redundant information between frames, pseudo video sequence compression method in image database incremental image compression effect when compared with the traditional compression encoding method to enhance co.. In order to overcome the pseudo video sequence compression encoding method and traditional compression encoding method, is proposed in this paper, first find the visual and incremental image most similar images from the image database, to find the image as the reference image, the incremental image compression encoding, which can make full use of the redundant information between frames, the compression effect to achieve the optimal.…”