Soft Compression: An Approach to Shape Coding for Images

Xin, Gangtao; Li, Zhefan; Zhu, Zheqi; Wan, Shuo; Fan, Pingyi; Letaief, Khaled B.

doi:10.1109/lcomm.2020.3035595

Cited by 11 publications

(23 citation statements)

References 8 publications

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“…We tested the soft compression algorithm for a binary image [ 1 ] on the MNIST [ 72 ] dataset. As expected, it had excellent results.…”

Section: Experimental Results and Theoretical Analysismentioning

confidence: 99%

“…We define

and

as the finite set of shapes and codewords with soft compression, respectively. Let T denote the total number of operations in the process of filling an image I [ 1 ]. Let

be the shape used in the i -th operation.…”

Section: Theorymentioning

confidence: 99%

“…The soft compression algorithm for a binary image was proposed in [ 1 ], which had excellent compression performance. Although [ 1 ] introduced the algorithm, they did not provide the theoretical analysis. We analyze the experimental results with compressible indicator function in Section 4 .…”

Section: Implementation Algorithmmentioning

confidence: 99%

“…All we can do is to make great efforts to get close to it. Xin et al [ 1 ] proposed soft compression based on information theory and statistical distribution. It uses shapes and locations to reflect the spatial correlation of an image, trying to achieve better compression performance.…”

Section: Introductionmentioning

confidence: 99%

“…Soft compression was first proposed in [ 1 ], using shapes and locations to represent a binary image. The set of shapes used in soft compression is not designed by experts, but searched from a dataset.…”

Section: Introductionmentioning

confidence: 99%

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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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“…We tested the soft compression algorithm for a binary image [ 1 ] on the MNIST [ 72 ] dataset. As expected, it had excellent results.…”

Section: Experimental Results and Theoretical Analysismentioning

confidence: 99%

“…We define

and

as the finite set of shapes and codewords with soft compression, respectively. Let T denote the total number of operations in the process of filling an image I [ 1 ]. Let

be the shape used in the i -th operation.…”

Section: Theorymentioning

confidence: 99%

Section: Implementation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soft Compression for Lossless Image Coding Based on Shape Recognition

Xin

Fan

2021

Entropy

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show abstract

Development of elevator vibration data collection system using simple compression encoding algorithm

Lin,

Hong,

Wang

et al. 2023

Soft Comput

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Robust, practical and comprehensive analysis of soft compression image coding algorithms for big data

Xin

Fan

2023

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With the advancement of intelligent vision algorithms and devices, image reprocessing and secondary propagation are becoming increasingly prevalent. A large number of similar images are being produced rapidly and widely, resulting in the homogeneity and similarity of images. Moreover, it brings new challenges to compression systems, which need to exploit the potential of deep features and side information of images. However, traditional methods are incompetent for this issue. Soft compression is a novel data-driven image coding algorithm with superior performance. Compared with existing paradigms, it has distinctive characteristics: from hard to soft, from pixels to shapes, and from fixed to random. Soft compression may hold promise for human-centric/data-centric intelligent systems, making them efficient and reliable and finding potential in the metaverse and digital twins, etc. In this paper, we present a comprehensive and practical analysis of soft compression, revealing the functional role of each component in the system.

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Soft Compression: An Approach to Shape Coding for Images

Cited by 11 publications

References 8 publications

Soft Compression for Lossless Image Coding Based on Shape Recognition

Soft Compression for Lossless Image Coding Based on Shape Recognition

Development of elevator vibration data collection system using simple compression encoding algorithm

Robust, practical and comprehensive analysis of soft compression image coding algorithms for big data

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