The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multi-scale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method. Objective image quality assessment research aims to design quality measures that can automatically predict perceived image quality. These quality measures play important roles in a broad range of applications such as image acquisition, compression, communication, restoration; enhancement, analysis, display, printing and watermarking. The most widely used full-reference image quality and distortion assessment algorithms are peak signal-to-noise ratio (PSNR) and mean squared error (MSE), w,hich do not correlate well with perceived quality (e.g., Traditional perceptual image quality assessment methods are based on a bottom-up approach which attempts to simulate the functionality of the relevant early human visual system (HVS) components. These' methods usually involve I ) a preprocessing process that may include image alignment, point-wise nonlinear transform, low-pass filtering that simulates eye optics, and color space transformation, 2) a clia,ineldecompositioii process that transforms the image signals into different spatial frequency as \\,ell as orientation selective subbands, 3) an error iiorniollzorion process that weights the error signal in each subband by incorporating the variation of visual sensitivity in different subbands, and the variation of visual error sensitivity caused by intra-or inter-channel neighboring transform coefficients, and 4) an eimr pooling process that combines the error signals in different subbands into a single qualilyidistortion value. While these bonom-up approaches can conveniently make use of many known psychophysical features of the HVS, it is important to recognize their limitations. In particular, the HVS is a complex and highly non-linear system and the complexity of natural images is also very significant, but most models of early vision are based on linear or quasi-linear operators that have been characterized using restricted and simplistic stimuli. Thus, these approaches must rely on a number of strong assumptions and generalizations [4], [5]. Furthermore, as the number of HVS features has increased, the resulting quality assessment systems have become too complicated to work with in real-world applications, especially for algorithm optimization purposes. Shuchrral sirnilany provides an alternative and complementary approach to the problem of image quality assessment [3]- [6] . It is based on a top-down assumption that the HVS is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity should bs a good approximation of perceived image quality. It has k e d shown that a simple implementation of this methodology, namely the smctural similarity (SSIM) index [SI, can outperform state-of-the-art perceptual image quality metrics. However, the SSIM index algorithm introduced in [SI is a single-scale approach. We consider this a drawback of the method because the right scale depends on viewing conditions (e.& display resolution and viewing distance). In this paper, we propose a ntulti-scale slructural similarity method and introduce a novel image synthesis-based approach to calibrate the parameters that weight the relative importance between different scales. Let x = {zili = 1 , 2 , . . . .N} and y = {yili = 1 , 2 , . . . ,N} be two discrete non-negative signals that have been aligned with each other (e&, two image patches extracted from the same spatial location from two images being compared, respectively), and let @=, 02 and oly be the mean of x, the variance of x, and the covariance of x and y, respectively. Approximately, pr and or can be viewed as estimates of the luminance and contrast of x, and oTy measures the the tendency of x and y lo vary together, thus an indication of structural similarity. In [5], the luminance, contrast and slructure comparison measures were given as follows: where CI, Cz and 6 3 are small constants given by C1 = (KI L) ' , Cz = (K2 L)' and Cz = C2/2,
Based on the SSIM, a multi-scale version for the original SSIM was proposed REF , in which the correlation, luminance and contract measures are also applied to filtered and down-sampled versions of the image.
Multiscale structural similarity for image quality assessment
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003
Computer Science