RVSIM: a feature similarity method for full-reference image quality assessment

Abstract

Image quality assessment is an important topic in the field of digital image processing. In this study, a full-reference image quality assessment method called Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM) is proposed. More precisely, a Log-Gabor filter is first used to decompose reference and distorted images, and Riesz transform is performed on the decomposed images on the basis of monogenic signal theory. Then, the monogenic signal similarity matrix is obtained by calculating the similarity of the local amplitude/phase/direction characteristics of monogenic signal. Next, we weight the summation of these characteristics with visual contrast sensitivity. Since the first-order Riesz transform cannot clearly express the corners and intersection points in the image, we calculate the gradient magnitude similarity between the reference and distorted images as a feature, which is combined with monogenic signal similarity to obtain a local quality map. Finally, we conduct the monogenic phase congruency using the Riesz transform feature matrix from the reference image and utilize it as a weighted function to derive the similarity index. Extensive experiments on five benchmark IQA databases, namely, LIVE, CSIQ, TID2008, TID2013, and Waterloo Exploration, indicate that RVSIM is a robust IQA method.

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

“…Depending on to what extent a reference image is used for quality assessment, existing objective IQA methods can be classified into three categories: full-reference (FR), reduced-reference (RR) and no-reference/blind (NR/B) methods. Accessing all or part of the reference image information is unrealistic in many circumstances [ 3 , 4 , 5 , 6 , 7 , 8 ], hence it has become increasingly important to develop effective blind IQA (BIQA) methods.…”

Dense Scale Invariant Feature Transform-Based Quality Assessment for Tone Mapping Image

“…In order to evaluate the performance of the proposed TM image quality evaluation model, we compare it with the five full-reference quality evaluation models for the ordinary images, i.e. RFSIM [4] , FSIM [5] , GMSD [6] , MDSI [7] , RVSIM [8] and the two full-reference quality evaluation models for the TM images, i.e. TMQI [9] , FSITM [11] .…”

“…The results are shown in Table I. [4] 0.4456 0.3957 0.2727 FSIM [5] 0.4885 0.3578 0.2420 GMSD [6] 0.5167 0.4167 0.2808 MDSI [7] 0.5643 0.4325 0.3143 RVSIM [8] 0.6187 0.4839 0.3452 TMQI [9] 0.7715 0.7407 0.5585 FSITM [11] 0.7496 0.7028 0.5160 Proposed 0.8045 0.7754 0.5912…”

“…At present, most of the full-reference quality evaluation methods for ordinary LDR images mainly include RFSIM [4] , FSIM [5] , GMSD [6] , MDSI [7] , RVSIM [8] , etc. Although the sensitive features for common distorted images are extracted such as gradient, phase consistency and contrast, the distortion characteristics of TM images are not taken into account, so the above methods are not directly applicable to TM image quality evaluation.…”

Distortion-specific feature selection algorithm for universal blind image quality assessment

“…Objective IQA is divided into full reference (FR), reduced reference (RR), and blind IQA techniques. FR-IQA techniques require the pristine version of the image to predict the quality score of images [2][3][4][5][6][7][8][9][10]. RR-IQA techniques *Correspondence: 12phdnizami@seecs.edu.pk 1 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan Full list of author information is available at the end of the article do not require the whole reference image but some information extracted from the reference image to perform IQA [11][12][13][14][15][16][17].…”