Shape Adaptive Texture Coding Based on Wavelet-Based Contourlet Transform

Shu, Zhenghua; Liu, Guodong; Xie, Zhen; Ren, Zhong

doi:10.1109/cisp-bmei.2018.8633164

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…But Symbol-Based coding is hard to generalize to other scenarios owing to the need of redesigning symbols. There are also some methods based on shape coding [23], [24], but none of them consider both shapes and locations at the same time. Fractal block coding [25] relies on the assumption that image redundancy can be efficiently exploited through self-transformability on a blockwise basis and it can approximate an original image by a fractal image.…”

Section: Related Workmentioning

confidence: 99%

Soft Compression for Lossless Image Coding

Xin,

Fan

2020

Preprint

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Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of information theory and statistical distribution. In this paper, we propose a new concept, compressible indicator function with regard to image, which gives a threshold about the average number of bits required to represent a location and can be used for revealing the performance of soft compression. We investigate and analyze soft compression for binary image, gray image and multi-component image by using specific algorithms and compressible indicator value. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images can be greatly reduced by applying soft compression.

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Section: Related Workmentioning

confidence: 99%

Soft Compression for Lossless Image Coding

Xin,

Fan

2020

Preprint

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“…However, symbol-based coding is hard to generalize to other scenarios, owing to the need of redesigning symbols. Some methods are also based on shape coding [ 58 , 59 ], but none of them consider both shapes and locations at the same time. Fractal block coding [ 60 ] relies on the assumption that image redundancy can be efficiently exploited through self-transformability on a blockwise basis and it can approximate an original image by a fractal image.…”

Section: Introductionmentioning

confidence: 99%

Soft Compression for Lossless Image Coding Based on Shape Recognition

Xin

Fan

2021

Entropy

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Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.

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“…The ability of multiscale geometric analysis (MGA) theory to process high-dimensional data is better than that of wavelet transform [1]. Since the birth of the JPEG2000 standard, researchers have conducted extensive research on image coding based on the MGA method, among which the most representative research results include image coding based on Ridgelet transform [2][3][4][5], Curvelet transform [6,7], Contourlet transform [8][9][10][11][12][13][14][15][16], Bandelet transform [17,18], and based on directional wavelet transform [19], etc. Besides, due to computational complexity and redundancy problems, these researches focused primarily on Contourlet transform.…”

Section: Introductionmentioning

confidence: 99%

A combined HMM–PCNN model in the contourlet domain for image data compression

Yang

et al. 2020

PLoS ONE

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Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, but also outdoes the limitations of wavelet transform in representing high-dimensional singular data such as edges and contours. Therefore, researchers have been exploring new MGA-based image compression standards rather than the JPEG2000 standard. However, due to the difference in terms of the data structure, redundancy and decorrelation between wavelet and MGA, as well as the complexity of the coding scheme, so far, no definitive researches have been reported on the MGA-based image coding schemes. In addressing this problem, this paper proposes an image data compression approach using the hidden Markov model (HMM)/pulse-coupled neural network (PCNN) model in the contourlet domain. First, a sparse decomposition of an image was performed using a contourlet transform to obtain the coefficients that show the multiscale and multidirectional characteristics. An HMM was then adopted to establish links between coefficients in neighboring subbands of different levels and directions. An Expectation-Maximization (EM) algorithm was also adopted in training the HMM in order to estimate the state probability matrix, which maintains the same structure of the contourlet decomposition coefficients. In addition, each state probability can be classified by the PCNN based on the state probability distribution. Experimental results show that the HMM/PCNN-contourlet model proposed in this paper leads to better compression performance and offer a more flexible encoding scheme.

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Shape Adaptive Texture Coding Based on Wavelet-Based Contourlet Transform

Cited by 4 publications

References 6 publications

Soft Compression for Lossless Image Coding

Soft Compression for Lossless Image Coding

Soft Compression for Lossless Image Coding Based on Shape Recognition

A combined HMM–PCNN model in the contourlet domain for image data compression

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