A New Method of Scalable Image Compression Coding

2013 ◽  
Vol 380-384 ◽  
pp. 3815-3817
Author(s):  
Yan Yang
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Region Of Interest ◽  
New Method ◽  
Original Image ◽  
High Quality ◽  
Memory Space ◽  
Limited Memory ◽  
Compression Coding

This paper presents a new method of scalable image compression coding based on the wavelet transform. This method delimits the region of interest of the original image, and give a high-quality encoding to this region and a rough encoding to the rest. The result shows that in the limited memory space, this algorithm provides the coarser reconstruct image to satisfy the basal subject quality. Using this method, we can give a high quality encoding to the region for interest.

scholarly journals A Fast Image Compression Algorithm Based on Wavelet Transform

2021 ◽  
Vol 15 ◽  
pp. 809-819
Author(s):  
Xiangjun Li ◽  
Shuili Zhang ◽  
Haibo Zhao
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Fractal Theory ◽  
Image Transmission ◽  
Reconstructed Image ◽  
High Quality ◽  
Fractal Compression ◽  
Compression Coding ◽  
Performance Of Algorithm

With multimedia becoming widely popular, the conflict between mass data and finite memory devices has been continuously intensified; so, it requires more convenient, efficient and high-quality transmission and storage technology and meanwhile, this is also the researchers’ pursuit for highly efficient compression technology and it is the fast image transmission that is what people really seek. This paper mainly further studies wavelet analysis and fractal compression coding, proposes a fast image compression coding method based on wavelet transform and fractal theory, and provides the theoretical basis and specific operational approaches for the algorithm. It makes use of the smoothness of wavelet, the high compression ratio of fractal compression coding and the high quality of reconstructed image. It firstly processes the image through wavelet transform. Then it introduces fractal features and classifies the image according to the features of image sub-blocks. Each class selects the proper features. In this way, for any sub-block, it only needs to search the best-matched block in a certain class according to the corresponding features. With this method, it can effectively narrow the search in order to speed up coding and build the relation of inequality between the sub-block and the matching mean square error. So, it can effectively combine wavelet transform with fractal theory and further improves the quality of reconstructed image. By comparing the simulation experiment, it objectively analyzes the performance of algorithm and proves that the proposed algorithm has higher efficiency.

Research and Progress of Image Compression Coding Based on Wavelet

2011 ◽  
Vol 403-408 ◽  
pp. 1352-1355
Author(s):  
Yue Li Cui ◽  
Zhi Gang Chen ◽  
Ai Hua Chen
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Visual System ◽  
Frequency Domain ◽  
Image Coding ◽  
Human Visual System ◽  
Original Image ◽  
Local Characteristics ◽  
Compression Coding ◽  
Research Hotspots

Image compression is a technology using as little as possible bits to represent the original image. As wavelet transform has local characteristics on the time and frequency domain, it makes up the deficiency of DCT. Moreover, its multi-resolution characteristics can easily associate with the human visual system (HVS). Besides, wavelet-based image compression is prone to combine with new image coding methods. It has become the research hotspots at present. This paper introduces wavelets theory and discusses the research status and progress of wavelet-based image compression then points out the main problems. Finally, the prospect in the future was presented.

Coding Algorithms of Aurora Image Compression Based on Wavelet Transformation

2012 ◽  
Vol 433-440 ◽  
pp. 5324-5328
Author(s):  
Guang Li Wu ◽  
Zhen Sen Wu ◽  
Shen Miao Han ◽  
Guang Ling Wu
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Wavelet Transformation ◽  
Wavelet Basis ◽  
Compression Effect ◽  
Main Factors ◽  
Compression Coding

This paper introduced some significant applications of aurora images, listed the main factors of choosing wavelet basis in image compression coding and analyzed the influence of aurora image compression effect caused by different wavelet basis were experimentally. The results of two kinds of significant wavelet transform algorithms EZW and SPIHT were analyzed, compared and also experimentally improved.

scholarly journals Comparative Study of Wavelet Image Compression: JPEG2000 Standart

2015 ◽  
Vol 16 (1) ◽  
pp. 83
Author(s):  
Ansam Ennaciri ◽  
Mohammed Erritali ◽  
Mustapha Mabrouki ◽  
Jamaa Bengourram
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Compression Ratio ◽  
Image Data ◽  
Discrete Wavelet ◽  
Original Image ◽  
Essential Quality ◽  
Image Data Compression ◽  
Wavelet Image

The objective of this paper is to study the main characteristics of wavelets that affect the image compression by using the discrete wavelet transform and lead to an image data compression while preserving the essential quality of the original image. This implies a good compromise between the image compression ratio and the PSNR (Peak Signal Noise Ration).

scholarly journals An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

2018 ◽  
Vol 29 (1) ◽  
pp. 1063-1078
Author(s):  
P. Sreenivasulu ◽  
S. Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Region Growing ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Huffman Encoding ◽  
Encoding Method

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

A Method for Graph Compression

2013 ◽  
Vol 662 ◽  
pp. 871-874
Author(s):  
Hai Tang Wang
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Calculation Method ◽  
New Method ◽  
Memory Space ◽  
Graph Compression ◽  
Mathematical Calculation

Image compression principle is to ensure a certain degree of image quality in the premise, a mathematical calculation method of the amount of data of the image to minimize. In order to more save memory space and more be used as a compressed representation of a image, a image is mapped into a graph, we give a new method for graph compression.

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