Color difference weighted adaptive residual preprocessing using perceptual modeling for video compression

Shaw, Mark Q.; Allebach, Jan P.; Delp, Edward J.

doi:10.1016/j.image.2015.04.008

Cited by 18 publications

(12 citation statements)

References 31 publications

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“…The practical application of color difference is mostly found in clinical dentistry, where the ability to reproduce the exact shade of natural teeth using restorative dental material is considered a challenging problem [51][52][53][54][55][56]. The other useful applications of color difference include contentbased retrieval [57], quality inspection of food [58,59], and video compression [60], but it has not been well explored for saliency based segmentation of melanoma skin lesion in dermoscopic images.…”

Section: Perceptual Color Differencementioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Section: Perceptual Color Differencementioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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“…Conversion from CIE XYZ to CIE La*b* [34,40,41] is accomplished using (2), (3) and (4) as discussed below:…”

Section: Cie La*b* Color Spacementioning

confidence: 99%

“…where X, Y, Z are calculated using (1) above and X n , Y n , Z n are the tri-stimulus values of the adapting white [34,40,41]…”

Section: Cie La*b* Color Spacementioning

confidence: 99%

“…In this paper, we use a perceptually uniform and linear CIE La*b* color space for compression of images. Though CIE La*b* color space has been used in past for compression of images/videos, it has been majorly used for pre-processing while the actual encoding/decoding is done in YCbCr color space [34,35]. As linear and perceptually uniform CIE La*b* color space is used, we use a linear CSF for generation of the quantization matrices.…”

Section: Introductionmentioning

confidence: 99%

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JPEG-based Variable Block-Size Image Compression using CIE Lab Color Space

2018

KSII TIIS

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In this work we propose a compression technique that makes use of linear and perceptually uniform CIE La*b* color space in the JPEG image compression framework to improve its performance at lower bitrates. To generate quantization matrices suitable for the linear and perceptually uniform CIE La*b* color space, a novel linear Contrast Sensitivity Function (CSF) is used. The compression performance in terms of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR), is further improved by utilizing image dependent, variable and non-uniform image sub-blocks generated using a proposed histogram-based merging technique. Experimental results indicate that the proposed linear CSF based quantization technique yields, on an average, 8% increase in CR for the same reconstructed image quality in terms of PSNR as compared to the conventional YCbCr color space. The proposed scheme also outperforms JPEG in terms of CR by an average of 45.01% for the same reconstructed image quality.

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“…the viewing distance or the ambient luminous contrast. Recently, the authors in [51] proposed an original method for preprocessing the residual signal in the spatial domain prior to transform, quantization and entropy coding. In order to do that, the authors introduce a HVS-based color contrast sensitivity model which accounts for visual saliency properties.…”

Section: Pre-processing For Video Compression: Background a Pre-mentioning

confidence: 99%

New adaptive filters as perceptual preprocessing for rate-quality performance optimization of video coding

Vidal¹,

Sturmel²,

Guillemot

et al. 2017

Signal Processing: Image Communication

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International audienceIn this paper, we introduce two perceptual filters as pre-processing techniques to reduce the bitrate of compressed high-definition (HD) video sequences at constant visual quality. The goal of these perceptual filters is to remove spurious noise and insignificant details from the original video prior to encoding. The proposed perceptual filters rely on two novel adaptive filters (called BilAWA and TBil) which combine the good properties of the bilateral and Adaptive Weighting Average (AWA) filters. The bilateral and AWA filters being initially dedicated to denoising, the behavior of the proposed BilAWA and TBil adaptive filters is first analyzed in the context of noise removal on HD test images. The first set of experimental results demonstrates their effectiveness in terms of noise removal while preserving image sharpness. A just noticeable distortion (JND) model is then introduced in the novel BilAWA and TBil filters to adaptively control the strength of the filtering process, taking into account the human visual sensitivity to signal distortion. Visual details which cannot be perceived are smoothed, hence saving bitrate without compromising perceived quality. A thorough experimental analysis of the perceptual JND-guided filters is conducted when using these filters as a pre-processing step prior to MPEG-4/AVC encoding. Psychovisual evaluation tests show that the proposed BilAWA pre-processing filter leads to an average bitrate saving of about 19.3% (up to 28.7%) for the same perceived visual quality. The proposed new pre-filtering approach has been also tested with the new state-of-the-art HEVC standard and has given similar efficiency in terms of bitrate savings for constant visual quality

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Color difference weighted adaptive residual preprocessing using perceptual modeling for video compression

Cited by 18 publications

References 31 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

JPEG-based Variable Block-Size Image Compression using CIE Lab Color Space

New adaptive filters as perceptual preprocessing for rate-quality performance optimization of video coding

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Color difference weighted adaptive residual preprocessing using perceptual modeling for video compression

Cited by 18 publications

References 31 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

JPEG-based Variable Block-Size Image Compression using CIE La*b* Color Space

New adaptive filters as perceptual preprocessing for rate-quality performance optimization of video coding

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JPEG-based Variable Block-Size Image Compression using CIE Lab Color Space