Nonlinear image representation for efficient perceptual coding

Malo, Jesús; Epifanio, Irene; Navarro, Rafael; Simoncelli, Eero P.

doi:10.1109/tip.2005.860325

Cited by 98 publications

(132 citation statements)

References 53 publications

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“…By normalizing signal power, channels become more independent 36 . A similar mechanism for reducing redundancy by divisive normalization has been proposed for visual processing [37][38][39] and can be used for image compression 40 . A channel with fixed dynamic range will communicate maximum information if the transfer function matches the cumulative density function (CDF) of the input variable 41 .…”

Section: Information Bottleneck and Gain Adaptationmentioning

confidence: 99%

Illusory percepts from auditory adaptation

Parra

Pearlmutter

2007

The Journal of the Acoustical Society of America

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Phenomena resembling tinnitus and Zwicker phantom tone are seen to result from an auditory gain adaptation mechanism that attempts to make full use of a fixed-capacity channel. In the case of tinnitus, the gain adaptation enhances internal noise of a frequency band otherwise silent due to damage. This generates a percept of a phantom sound as a consequence of hearing loss. In the case of Zwicker tone, a frequency band is temporarily silent during the presentation of a notched broad-band sound, resulting in a percept of a tone at the notched frequency. The model suggests a link between tinnitus and the Zwicker tone percept, in that it predicts different results for normal and tinnitus subjects due to a loss of instantaneous nonlinear compression. Listening experiments on 44 subjects show that tinnitus subjects (11 of 44) are significantly more likely to hear the Zwicker tone. This psychoacoustic experiment establishes the first empirical link between the Zwicker tone percept and tinnitus. Together with the modeling results, this supports the hypothesis that the phantom percept is a consequence of a central adaptation mechanism confronted with a degraded sensory apparatus.

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Section: Information Bottleneck and Gain Adaptationmentioning

confidence: 99%

Illusory percepts from auditory adaptation

Parra

Pearlmutter

2007

The Journal of the Acoustical Society of America

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“…It has also been found to be powerful in modeling the neuronal responses in the visual cortex [36,37]. Divisive normalization has been successfully applied in IQA [38,39], image coding [40], video coding [31] and image denoising [41]. Equation (14) suggests that the threshold is chosen adaptively for each patch.…”

Section: Ssim-optimal Local Model From Sparse Representationmentioning

confidence: 99%

SSIM-inspired image restoration using sparse representation

Rehman

Rostami

Wang

et al. 2012

EURASIP J. Adv. Signal Process.

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Recently, sparse representation based methods have proven to be successful towards solving image restoration problems. The objective of these methods is to use sparsity prior of the underlying signal in terms of some dictionary and achieve optimal performance in terms of mean-squared error, a metric that has been widely criticized in the literature due to its poor performance as a visual quality predictor. In this work, we make one of the first attempts to employ structural similarity (SSIM) index, a more accurate perceptual image measure, by incorporating it into the framework of sparse signal representation and approximation. Specifically, the proposed optimization problem solves for coefficients with minimum L 0 norm and maximum SSIM index value. Furthermore, a gradient descent algorithm is developed to achieve SSIM-optimal compromise in combining the input and sparse dictionary reconstructed images. We demonstrate the performance of the proposed method by using image denoising and super-resolution methods as examples. Our experimental results show that the proposed SSIM-based sparse representation algorithm achieves better SSIM performance and better visual quality than the corresponding least square-based method.

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“…Examples of successful characterization of post-transforms relations include texture synthesis [1], image coding [2,3], or image denoising [4].…”

Section: Introductionmentioning

confidence: 99%

PCA Gaussianization for image processing

Laparra

Camps‐Valls

Malo

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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The estimation of high-dimensional probability density functions (PDFs) is not an easy task for many image processing applications. The linear models assumed by widely used transforms are often quite restrictive to describe the PDF of natural images. In fact, additional non-linear processing is needed to overcome the limitations of the model. On the contrary, the class of techniques collectively known as projection pursuit, which solve the high-dimensional problem by sequential univariate solutions, may be applied to very general PDFs (e.g. iterative Gaussianization procedures). However, the associated computational cost has prevented their extensive use in image processing.In this work, we propose a fast alternative to iterative Gaussianization methods that makes it suitable for image processing while ensuring its theoretical convergence. Method performance is successfully illustrated in image synthesis and classification problems.

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Nonlinear image representation for efficient perceptual coding

Cited by 98 publications

References 53 publications

Illusory percepts from auditory adaptation

Illusory percepts from auditory adaptation

SSIM-inspired image restoration using sparse representation

PCA Gaussianization for image processing

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