scholarly journals Soft Compression for Lossless Image Coding Based on Shape Recognition

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1680
Author(s):  
Gangtao Xin ◽  
Pingyi Fan
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Storage Space ◽  
Compression Method ◽  
Lossless Image Compression ◽  
Working Principle ◽  
Spatial Redundancy ◽  
And Storage ◽  
Component Image ◽  
Do So

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.

scholarly journals Lossless Image Compression Based on Multiple-Tables Arithmetic Coding

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Rung-Ching Chen ◽  
Pei-Yan Pai ◽  
Yung-Kuan Chan ◽  
Chin-Chen Chang
Keyword(s):  
Image Compression ◽  
High Performance ◽  
Arithmetic Coding ◽  
Edge Detector ◽  
Compression Method ◽  
Lossless Image Compression ◽  
Code Table ◽  
Gray Level Image ◽  
Encoding Method ◽  
Spatial Redundancy

This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC) method to encode a gray-level imagef. First, the MTAC method employs a median edge detector (MED) to reduce the entropy rate off. The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image. To promote high performance of the arithmetic encoding method, the MTAC method first classifies the data and then encodes each cluster of data using a distinct code table. The experimental results show that, in most cases, the MTAC method provides a higher efficiency in use of storage space than the lossless JPEG2000 does.

scholarly journals A PREDICTIVE CODING METHOD FOR LOSSLESS COMPRESSION OF IMAGES

2013 ◽  
Vol 7 (3) ◽  
pp. 683-685
Author(s):  
Anil Mishra ◽  
Ms. Savita Shiwani
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Video Processing ◽  
Prediction Method ◽  
Original Form ◽  
Minimum Entropy ◽  
Digital World ◽  
Adaptive Prediction ◽  
Continuous Tone ◽  
Spatial Redundancy

Images are an important part of today's digital world. However, due to the large quantity of data needed to represent modern imagery the storage of such data can be expensive. Thus, work on efficient image storage (image compression) has the potential to reduce storage costs and enable new applications.This lossless image compression has uses in medical, scientific and professional video processing applications.Compression is a process, in which given size of data is compressed to a smaller size. Storing and sending images to its original form can present a problem in terms of storage space and transmission speed.Compression is efficient for storing and transmission purpose.In this paper we described a new lossless adaptive prediction based algorithm for continuous tone images. In continuous tone images spatial redundancy exists.Our approach is to develop a new backward adaptive prediction techniques to reduce spatial redundancy in a image.The new prediction technique known as Modifed Gradient Adjusted Predictor (MGAP) is developed. MGAP is based on the prediction method used in Context based Adaptive Lossless Image Coding (CALIC). An adaptive selection method which selects the predictor in a slope bin in terms of minimum entropy improves the compression performance.

Implementation of LOCO-I Lossless Image Compression Algorithm for Deep Space Applications

2014 ◽  
Vol 3 (3) ◽  
pp. 98
Author(s):  
P. Praveena
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Low Complexity ◽  
Vital Role ◽  
Space Science ◽  
Compression Algorithm ◽  
Deep Space ◽  
Space Applications ◽  
Lossless Image Compression ◽  
Image Compression Algorithm

<p>Present emerging trend in space science applications is to explore and utilize the deep space. Image coding in deep space communications play vital role in deep space missions. Lossless image compression has been recommended for space science exploration missions to retain the quality of image. On-board memory and bandwidth requirement is reduced by image compression. Programmable logic like field programmable gate array (FPGA) offers an attractive solution for performance and flexibility required by real time image compression algorithms. The powerful feature of FPGA is parallel processing which allows the data to process quicker than microprocessor implementation. This paper elaborates on implementing low complexity lossless image compression algorithm coder on FPGA with minimum utilization of onboard resources for deep space applications.</p>

scholarly journals Backpropagation Neural Network Implementation for Medical Image Compression

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kamil Dimililer
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Compression Ratio ◽  
Haar Wavelet ◽  
Compression Method ◽  
High Compression Ratio ◽  
Haar Wavelet Transform ◽  
X Ray ◽  
Ideal Image ◽  
And Storage

Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

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