Human Visual System Based Wavelet Decomposition for Image Compression

O'Rourke, Thomas P.; Stevenson, Robert Louis

doi:10.1006/jvci.1995.1010

Cited by 27 publications

(14 citation statements)

References 10 publications

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“…The human visual system plays an important role in the perceived image quality of compressed images [37,38]. System designers and users should be able to take advantage of the current knowledge of visual perception, i.e.…”

Section: Iv4 Visual Frequency Weightingmentioning

confidence: 99%

The JPEG2000 still image coding system: an overview

Christopoulos

Skodras

Ebrahimi

2000

IEEE Trans. Consumer Electron.

1,178

536

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--With the increasing use of multimedia technologies, image compression requires higher performance as well as new features. To address this need in the specific area of still image encoding, a new standard is currently being developed, the JPEG2000. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and functionalities that current standards can either not address efficiently or in many cases cannot address at all. Lossless and lossy compression, embedded lossy to lossless coding, progressive transmission by pixel accuracy and by resolution, robustness to the presence of bit-errors and region-of-interest coding, are some representative features. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, color facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce.

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Section: Iv4 Visual Frequency Weightingmentioning

confidence: 99%

The JPEG2000 still image coding system: an overview

Christopoulos

Skodras

Ebrahimi

2000

IEEE Trans. Consumer Electron.

1,178

536

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“…Rourke and Stevenson [26] have used diamond shaped wavelet filters for image compression. They have shown that this technique incorporates some of the properties of human visual system.…”

Section: Rotated M-band Dual Tree Complex Wavelets (Mband_dt_rcwt)mentioning

confidence: 99%

Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image Retrieval

Jasmine¹,

Kumar²

2014

IJIGSP

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Abstract-In this paper, a novel algorithm which integrates the RGB color histogram and texture features for content based image retrieval. A new set of twodimensional (2-D) M-band dual tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT are designed to improve the texture retrieval performance. Unlike the standard dual tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, two texture databases are used. Further, it is mentioned that the databases used are Brodatz gray scale database and MIT VisTex Color database. The retrieval efficiency and accuracy using proposed features is found to be superior to other existing methods.

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“…The most common HVS phenomena considered are the luminance and frequency sensitivities [2], [4], [19], [25], [28], [30]. Visual masking has been used successfully in predictive coding [11], [42], vector quantization [15], and to some extent in block transform coding [51].…”

Section: B Hvs Properties Used In Image Compressionmentioning

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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Human Visual System Based Wavelet Decomposition for Image Compression

Cited by 27 publications

References 10 publications

The JPEG2000 still image coding system: an overview

The JPEG2000 still image coding system: an overview

Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image Retrieval

A wavelet visible difference predictor

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