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/.

We have implemented a model of contrast gain control in human vision which incorporates a number of key features, including a contrast sensitivity function, multiple oriented band-pass channels, accelerating nonlinearities, and a divisive inhibitory gain-control pool. The parameters of this model have been optimized through a fit to the recent data that describe masking of a Gabor function by cosine and Gabor masks [Foley, J. M. (1994). Human luminance pattern mechanisms: masking experiments require a new model. Journal of the Optical Society of America A 11(6), 1710-1719]. The model achieves a good fit to the data. We also demonstrate how the concept of recruitment may accommodate a variant of this model in which excitatory and inhibitory paths share a common accelerating non-linearity, but which include multiple channels tuned to different levels of contrast [Teo, P. C. & Heeger, D. J. (1994). Perceptual image distortion. Proceedings,

Objective image and video quality measures play important roles in a variety of image and video processing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural distortion as an estimate of perceived visual distortion. A computationally e#cient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group (VQEG) Phase I FR-TV test data set.

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 propose a new measure of perceptual image quality based on a multiple channel human visual system (HVS) model for use in digital image compression. The model incorporates the HVS light sensitivity, spatial frequency and orientation sensitivity, and masking effects. The model is based on the concept of local band-limited contrast (LBC) in oriented spatial frequency bands. This concept leads to a simple masking function. The model has the flexibility to account for the changes in frequency sensitivity as a function of local luminance and is consistent with masking experiments using gratings and edges. Numerical scaling experiments with a test panel and a set a test images that were coded using different coding algorithms showed that the proposed measure correlates better with perceptual image quality than the conventional SNR measure. 1. INTRODUCTION In optimization and evaluation of digital image compression algorithms, the signal to noise ratio (SNR) is generally used as a measure...

Abstract not found

www.cis.rit.edu/mcsl

Reduced-reference (RR) image quality measures aim to predict the visual quality of distorted images with only partial information about the reference images. In this paper, we propose an RR image quality assessment method based on a natural image statistic model in the wavelet transform domain. We use the Kullback-Leibler distance between the marginal probability distributions of wavelet coefficients of the reference and distorted images as a measure of image distortion. A generalized Gaussian model is employed to summarize the marginal distribution of wavelet coefficients of the reference image, so that only a relatively small number of RR features are needed for the evaluation of image quality. The proposed method is easy to implement and computationally efficient. In addition, we find that many well-known types of image distortions lead to significant changes in wavelet coefficient histograms, and thus are readily detectable by our measure. A Matlab implementation of the method has been made available online at

We propose a new model for the prediction of distortion visibility in digital image sequences, which is aimed at use in digital video compression algorithms. The model is an extension of our spatial vision model with a spatio-temporal contrast sensitivity function and an eye movement estimation algorithm. Due to the importance of smooth pursuit eye movements when viewing image sequences, eye movements cannot be neglected in a spatio-temporal vision model. Although eye movements can be incorporated by motion compensation of the contrast sensitivity function, the requirements for this motion compensation are different than those for motion compensated prediction in video coding. We propose an algorithm for the estimation of smooth pursuit eye movements, under the worst-case assumption that the observer is capable of tracking all objects in the image. In image and image sequence compression, models which predict the visibility of coding distortions can be used to improve the visual quality of the total compression system. Many models have been proposed for the processing of spatial information in the human visual system to predict distortion visibility in coded images [1-5]. However, the number