Wavelet transform in human visual channels

Gaudart, L.; Crebassa, J.; Petrakian, J.P.

doi:10.1364/ao.32.004119

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…Considerations of this nature led researchers to fomulate a variety of multichannel neuronal models consisting of a set of directional Gabor filters with a hierarchical wavelet-like organization [23], [24], [ 1081, [77]. Simpler decompositions in terms of wavelet-basis have also been considered [34].…”

Section: E Wavelets As a Model Of Perceptionmentioning

confidence: 99%

A review of wavelets in biomedical applications

1996

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In this paper, we present an overview of the various uses of the wavelet transform (WT) in medicine and biology. We start by describing the wavelet properties that are the most important f o r biomedical applications. In particular, we provide an interpretation of the continuous wavelet transfom (CWT) as a prewhitening multiscale matched filter. We also briefy indicate the analogy between the WT and some of the biological processing that occurs in the early components of the auditory and visual system. We then review the uses of the WT f o r the analysis of I-D physiological signals obtained by phonocardiography, electrocardiography (ECG), and electroencephalography (EEG), including evoked response potentials. Next, we provide a survey of recent wavelef developments in medical imaging. These include biomedical image processing algorithms (e.g., noise reduction, image enhancement, and detection of microcalciJcations in ntammograms), image reconstruction and acquisition schemes (tomography, and magnetic resonance imaging (MRI)), and multiresolution methods for the registration and statistical analysis of functional images of the brain (positron emission tomography (PET) and functional MRI ( f l R I ) ) . In each case, we provide the reader with some general background information and a brief explanation of how the methods work.

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Section: E Wavelets As a Model Of Perceptionmentioning

confidence: 99%

A review of wavelets in biomedical applications

1996

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“…The similarities between the wavelet transform and multiple channel models of the HVS have been noted before [9], [23], [47]. In addition, a number of desirable properties of a spatial-frequency hierarchy used to model the HVS have been discussed [7], [50].…”

Section: Wavelets and The Hvsmentioning

confidence: 98%

“…Part of the reason for this is its similarities to the multiple channel models of the HVS [9], [23], [47]. In particular, both decompose the image into a number of spatial frequency channels that respond to an explicit spatial location, a limited band of frequencies, and a limited range of orientations.…”

Section: Wavelets and The Hvsmentioning

confidence: 99%

A wavelet visible difference predictor

Bradley

1999

IEEE Trans. on Image Process.

131

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Abstract-In this paper, we describe a model of the human visual system (HVS) based on the wavelet transform. This model is largely based on a previously proposed model, but has a number of modifications that make it more amenable to potential integration into a wavelet based image compression scheme. These modifications include the use of a separable wavelet transform instead of the cortex transform, the application of a wavelet contrast sensitivity function (CSF), and a simplified definition of subband contrast that allows us to predict noise visibility directly from wavelet coefficients. Initially, we outline the luminance, frequency, and masking sensitivities of the HVS and discuss how these can be incorporated into the wavelet transform. We then outline a number of limitations of the wavelet transform as a model of the HVS, namely the lack of translational invariance and poor orientation sensitivity. In order to investigate the efficacy of this wavelet based model, a wavelet visible difference predictor (WVDP) is described. The WVDP is then used to predict visible differences between an original and compressed (or noisy) image. Results are presented to emphasize the limitations of commonly used measures of image quality and to demonstrate the performance of the WVDP. The paper concludes with suggestions on how the WVDP can be used to determine a visually optimal quantization strategy for wavelet coefficients and produce a quantitative measure of image quality.Index Terms-Human visual system, image compression, image quality, visible difference prediction, wavelet transform.

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“…Masking may occur in spatial domain or in temporal domain. The wavelet transform can decompose the image into a number of spatial frequency channels that respond to an explicit spatial location, with a limited band of frequency and orientation range [4]. Such a structure of sub-band decomposition is very useful for one to exploit the contrast sensitivity of the errors in different bands and visual masking effect.…”

Section: Some Fundamental Properties Of the Hvsmentioning

confidence: 99%

A Wavelet-Based Visible Distortion Measure for Video Quality Evaluation

Yao

Wang

Ong

et al. 2006

2006 International Conference on Image Processing

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In this paper, we propose a new video quality evaluation method, which measures the perceived error between original and distorted images using wavelet justnoticeable-distortion (JND) profile. The error of the subband coefficient that is below the visual detection threshold is ignored while some errors that take place in the content-activity regions are masked. Luminance masking and temporal contrast masking are integrated to adjust the perceived error. Experiments on VQEG (video quality expert group) test sequences show that the proposed method can achieve good correlation with subjective evaluation.

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Wavelet transform in human visual channels

Cited by 26 publications

References 19 publications

A review of wavelets in biomedical applications

A review of wavelets in biomedical applications

A wavelet visible difference predictor

A Wavelet-Based Visible Distortion Measure for Video Quality Evaluation

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