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/.

Abstract not found

While lossy image compression techniques are vital in reducing bandwidth and storage requirements, they result in distortions in compressed images. A reliable quality measure is a much needed tool for determining the type and amount of image distortion. The traditional subjective criteria, which involve human observers, are inconvenient, time-consuming, and influenced by environmental conditions. Widely used pixelwise measures such as the mean square error (MSE) cannot capture the artifacts like blurriness or blockiness, and do not correlate well with visual error perception. Attempts to improve quality measurement include incorporation of simple models of the human visual system (HVS) and multi-dimensional tool design. We review the criteria for monochrome compressed image quality from 1974 to 1999.

The important criteria used in subjective evaluation of distorted images include the amount of distortion, the type of distortion, and the distribution of error. An ideal image quality measure should therefore be able to mimic the human observer. We present a new image quality measure that can be used as a multidimensional or a scalar measure to predict the distortion introduced by a wide range of noise sources. Based on the Singular Value Decomposition, it reliably measures the distortion not only within a distortion type at different distortion levels but also across different distortion types. The measure was applied to Lena using six types of distortion (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening and DC-shifting), each with five distortion levels.

This paper describes an objective quality measure for image sequences based on a model of the human visual system (HVS). When developing an appropriate HVS based model it is important to consider the structure of the pictures and of the coding errors introduced by the compression scheme. This is because they are both unlike the simple and artificial stimuli used in psychophysical tests to obtain HVS parameters. An analysis of the important features of coded pictures is given, and a perceptual quality model incorporating these features is described. Additional uses of a multi-dimensional quality measure are also discussed.

The important criteria used in subjective evaluation of distorted images include the amount of distortion, the type of distortion, and the distribution of error. An ideal image quality measure should therefore be able to mimic the human observer. We present a new gray-scale image quality measure that can be used as a graphical or a scalar measure to predict the distortion introduced by a wide range of noise sources. Based on the Singular Value Decomposition, it reliably measures the distortion not only within a distortion type at different distortion levels but also across different distortion types. The measure was applied to five test images (Airplane, Boat, Goldhill, Lena, and Peppers) using six types of distortion (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening, and DC-shifting), each with five distortion levels. Its performance is compared with PSNR and two recent measures.

A blockwise distortion measure is proposed for evaluating the visual quality of compressed images. The proposed measure calculates quantitatively how well important visual properties have been preserved in the distorted image. The method consists of three quality factors detecting contrast errors, structural errors, and quantization errors. The proposed method outperforms PQS for a set of test images, and is much simpler to implement. The method should also be applicable to color images; properties like color richness and saturation are captured by the quantization and contrast measures respectively.

The paper covers an overview of image compression methods that are commonly used in digital telecommunication systems. A proposal is made how adaptive optimisation schemes can be applied in order to provide an optimal level of service quality. The criterion of optimality is set by a currently available network bandwidth, while providing a user with best possible quality of service. The problem, described in the paper, is how to select optimal image compression and transmission parameters, which employ currently available bandwidth and maintains perceptual degradation at a minimum level. The optimisation is constrained by minimum quality of service requirements. 1

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