Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.

Objective methods for assessing perceptual image quality traditionally attempt to quantify the visibility of errors (di#erences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MatLab implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.

this paper, we will use f(n) to denote the value (intensity, grayscale, etc.) of an image pixel at location n. Usually the image pixels are arranged in a Cartesian March 14, 1999 11 grid and n = (n 1

We describe experimental tests of a spatial extension to the CIELAB color metric for measuring color reproduction errors of digital images. The standard CIELAB DEmetric is suitable for use on large uniform color targets, but not on images, because color sensitivity changes as a function of spatial pattern. The S-CIELAB extension includes a spatial processing step, prior to the CIELAB DE calculation, so that the results correspond better to color difference perception by the human eye. The S-CIELAB metric was used to predict texture visibility of printed halftone patterns. The results correlate with perceptual data better than standard CIELAB and point the way to various improvements.

As the healthcare community has begun to rely increasingly upon digital technologies for acquisition, storage, and transmission of pictorial data, image compression has become an indispensable tool. We have investigated the feasibility of lossy compression in a well defined task domain, the clinical assessment of digitized images of chromatic, microscopic pathology specimens. The effect of compression was measured under two distinct perceptual criteria, just noticeable difference (j.n.d.) and largest tolerable distortion (l.t.d.), differing in the involvement required from subjects, who were experts in pathology. For standard JPEG compressed images it was found that when the experiment is performed under the l.t.d. criterion, a significantly larger compression ratio is reported as satisfactory. It is concluded that lossy compression holds promise for diagnostic telepathology. -- To whom correspondence should be addressed. E-mail: djf@pleiad.umdnj.edu 2 1 Introduction The dramatic g...

We review two classes of image analysis tools based on single and multiple channel models of human vision processing, designed to predict the visibility of printed dots and halftone texture, respectively. 1 Introduction Nearly every imaging peripheral Hewlett-Packard manufactures is designed to optimize subjective image quality. Whether we are capturing, transmitting or rendering visual images, we consider the impact that our design decisions have upon subjective image quality. Subjective impressions of image quality, however, are difficult, if not impossible, to measure, quantify and predict. Subjective image quality is influenced by personal experience and other high-level factors outside of any simple physical description of the image. Thus we are faced with the challenging task of optimizing a signal that we find difficult to measure. Some physical attributes of an image can be strongly correlated with perceived quality. Our approach to image quality evaluation is to identify the...

Humans have always seen the world in color. In the last three decades, there has been rapid and enormous transition from grayscale images to color ones. Well-known objective evaluation algorithms for measuring image quality include mean squared error (MSE), peak signal-to-noise ratio (PSNR), and human Visual System based one are structural similarity measures and edge based similarity measures. One of the common and major limitations of these objective measures is that they evaluate the quality of grayscale images only and don’t make use of image color information. Since, Color is a powerful descriptor that often simplifies the object identification and extraction from a scene so color information also could influence human beings ’ judgments. So, in this paper new objective color image quality measure in spatial domain is proposed that overcomes the limitation of these existing methods significantly, is easy to calculate and applicable to various image processing applications. The proposed quality measure has been designed as a combination of four main factors: luminance similarity, structure correlation, edge similarity, and color similarity. This proposed index is mathematically defined and in it HVS model is explicitly employed. Experiments on various image distortion types indicate that this index performs significantly better than other traditional error summation methods and existing similarity measures.

The Blue Noise Mask (BNM) is a halftone screen that produces unstructured visually pleasing dot patterns. The BNM combines the blue-noise characteristics of error diffusion and the simplicity of ordered dither. A BNM is constructed by designing a set of interdependent binary patterns for individual gray levels. In this paper, we investigate the quality issues in blue-noise binary pattern design and mask generation as well as in application to color reproduction. Using a global filtering technique and a local "force" process for rearranging black and white pixels, we are able to generate a series of binary patterns, all representing a certain gray level, ranging from white-noise pattern to highly structured pattern. The quality of these individual patterns are studied in terms of low-frequency structure and graininess. Typically, the low-frequency structure (LF) is identified with a measurement of the energy around DC in the spatial frequency domain, while the graininess is quantified b...

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