Showing papers by "Zhou Wang published in 2009"

Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

Abstract: In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.

Complex Wavelet Structural Similarity: A New Image Similarity Index

Abstract: We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.

Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation

Abstract: Reduced-reference image quality assessment (RRIQA) methods estimate image quality degradations with partial information about the ldquoperfect-qualityrdquo reference image. In this paper, we propose an RRIQA algorithm based on a divisive normalization image representation. Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that significantly improves statistical independence for natural images. By using a Gaussian scale mixture statistical model of image wavelet coefficients, we compute a divisive normalization transformation (DNT) for images and evaluate the quality of a distorted image by comparing a set of reduced-reference statistical features extracted from DNT-domain representations of the reference and distorted images, respectively. This leads to a generic or general-purpose RRIQA method, in which no assumption is made about the types of distortions occurring in the image being evaluated. The proposed algorithm is cross-validated using two publicly-accessible subject-rated image databases (the UT-Austin LIVE database and the Cornell-VCL A57 database) and demonstrates good performance across a wide range of image distortions.

Introduction to the Issue on Visual Media Quality Assessment

Abstract: The 12 papers in this special issue cover various aspects of visual media quality assessment.

Image Quality Assessment

Abstract: Publisher Summary This chapter examines objective criteria for the evaluation of image quality as perceived by an average human observer The focus is on image fidelity, ie, how close an image is to a given original or reference image This paradigm of image quality assessment (QA) is also known as full reference image QA Three classes of image QA algorithms that correlate with visual perception significantly better are discussed—human vision based metrics, Structural SIMilarity (SSIM) metrics, and information theoretic metrics Each of these techniques approaches the image QA problem from a different perspective and using different first principles In addition to these QA techniques, this chapter also highlights the similarities, dissimilarities, and interplay between these seemingly diverse techniques

The Use of Residuals in Image Denoising

Abstract: State-of-the-art image denoising algorithms attempt to recover natural image signals from their noisy observations, such that the statistics of the denoised image follow the statistical regularities of natural images. One aspect generally missing in these approaches is that the properties of the residual image (defined as the difference between the noisy observation and the denoised image) have not been well exploited. Here we demonstrate the usefulness of residual images in image denoising. In particular, we show that well-known full-reference image quality measures such as the mean-squared-error and the structural similarity index can be estimated from the residual image without the reference image. We also propose a procedure that has the potential to enhance the image quality of given image denoising algorithms.

Quantifying color image distortions based on adaptive spatio-chromatic signal decompositions

Abstract: We describe a framework for quantifying color image distortion based on an adaptive signal decomposition. Specifically, local blocks of the image error are decomposed using a set of spatio-chromatic basis functions that are adapted to the spatial and color structure of the original image. The adaptive functions are chosen to isolate specific distortions such as luminance, hue, and saturation changes. These adaptive basis functions are used to augment a generic orthonormal basis, and the overall distortion is computed from the weighted sum of the coefficients of the resulting overcomplete decomposition, with smaller weights chosen for the adaptive terms. A set of preliminary experiments show that the proposed distortion measure is consistent with human perception of color images subjected to a variety of different common distortions. The framework may be easily extended to include any form of continuous spatio-chromatic distortion.

Statistics of natural image sequences: temporal motion smoothness by local phase correlations

Abstract: Statistical modeling of natural image sequences is of fundamental importance to both the understanding of biological visual systems and the development of Bayesian approaches for solving a wide variety of machine vision and image processing problems. Previous methods are based on measuring spatiotemporal power spectra and by optimizing the best linear filters to achieve independent or sparse representations of the time-varying image signals. Here we propose a different approach, in which we investigate the temporal variations of local phase structures in the complex wavelet transform domain. We observe that natural image sequences exhibit strong prior of temporal motion smoothness, by which local phases of wavelet coefficients can be well predicted from their temporal neighbors. We study how such a statistical regularity is interfered with "unnatural" image distortions and demonstrate the potentials of using temporal motion smoothness measures for reduced-reference video quality assessment.

Multifocus Image Fusion Using Local Phase Coherence Measurement

Abstract: Image fusion is the task of enhancing the perception of a scene by combining information captured by different imaging sensors. A critical issue in the design of image fusion algorithms is to define activity measures that can evaluate and compare the local information content of multiple images. In doing so, existing methods share a common assumption that high local energy or contrast is a direct indication for local sharpness. In practice, this assumption may not hold, especially when the images are captured using different instrument modalities. Here we propose a complex wavelet transform domain local phase coherence measure to assess local sharpness. A novel image fusion method is then proposed to achieve both maximal contrast and maximal sharpness simultaneously at each spatial location. The proposed method is computationally efficient and robust to noise, which is demonstrated using both synthetic and real images.

Multi-sensor image registration based-on local phase coherence

Abstract: The major challenges in automatic multi-sensor image registration are the inconsistency in intensity or contrast patterns, and the existence of partial or missing information between images. Here we propose a novel image registration method based on local phase coherence features, which are insensitive to changes in intensity or contrast. Furthermore, a new objective function based on weighted mutual information is proposed, where less weight is given to the objects that have no correspondence between images. The proposed method has been tested on both synthetic and medical images and evaluated based on registration accuracy. Our experiments demonstrate good performance of the proposed approach with missing or partial data, with significant changes in contrast, and with the presence of noise.