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

Abstract

International audiencePerceptual image quality assessment (IQA) uses a computational model to assess the image quality in a fashion consistent with human opinions. A good IQA model should consider both the effectiveness and efficiency. To meet this need, a new model called multiscale contrast similarity deviation (MCSD) is developed in this paper. Contrast is a distinctive visual attribute closely related to the quality of an image. To further explore the contrast features, we resort to the multiscale representation. Although the contrast and the multiscale representation have already been used by other IQA indices, few have reached the goals of effectiveness and efficiency simultaneously. We compared our method with other state-of-the-art methods using six well-known databases. The experimental results showed that the proposed method yielded the best performance in terms of correlation with human judgments. Furthermore, it is also efficient when compared with other competing IQA models

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

“…In order to eliminate the performance bias, random-test is repeated 1000 times and the mean value across these 1000 iterations is reported as the final result in this paper. Table 2 shows the overall performance comparison with state-of-the-art IQA methods including MCSD [1], IFS [4], LCSIM3 [5], CLFE [6], CLR [8], VSI [9], SSIM [20], GMSD [25], ESIM2 [37], and QASD [38] on different databases. For SSIM, an alternative framework based on structural similarity is introduced to evaluate the quality of images.…”

“…It is discovered that human eyes can easily identify and process the natural images at different scales [1]. Therefore, processing a natural image at different scales can increase the flexibility and adaptation for the image quality evaluation [30].…”

“…Table 4 lists the SRCC comparison of the proposed method with others for all five individual distortion types on LIVE database. The compared methods include MCSD [1], BIFS [3], LCSIM [5], GLD-SR [7], CLR [8], VSI [9], SSIM [20], GMSD [25], CSV [40], and SURF [41]. In Table 4, the top performance for each distortion type is highlighted in boldface, too.…”

“…With the rapid development of multimedia technology, millions of digital images require to be processed; however, any operations on images, such as image acquisition, reproduction, compression, storage, transmission, and restoration, may introduce noise into images [1,2]. Image quality assessment (IQA) is employed to estimate the degree of distortion.…”

Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment

“…Great efforts have been made to develop more comprehensive features to accurately quantify human's subjective perception upon image quality. The implementation of more complicated feature extraction schemes can be found in more recent works [10,11]. Numerous studies have confirmed that the desirable image features for IQA should be relevant to image quality closely and correlate well with human's subjective sensation [5].…”