scholarly journals Comprehensive assessment of image compression algorithms

2020 ◽  
Author(s):  
Michela Testolina ◽  
Evgeniy Upenik ◽  
Touradj Ebrahimi
Keyword(s):  
Image Compression ◽  
Comprehensive Assessment ◽  
Compression Algorithms

Efficient Image Compression Algorithms Using Evolved Wavelets

2011 ◽  
Vol 1 (4) ◽  
pp. 1-8
Author(s):  
G. Chenchu Krishnaiah ◽  
T. Jayachandraprasad ◽  
M.N. Giri Prasad
Keyword(s):  
Image Compression ◽  
Compression Algorithms

ESTIMATION OF HYBRID IMAGE COMPRESSION ALGORITHMS FOR UNDERWATER IMAGES

2018 ◽  
Vol 5 (4) ◽  
pp. 34
Author(s):  
R. PANDIAN ◽  
KUMARI S. LALITHA ◽  
KUMAR R. RAJA ◽  
RAVIKUMAR D. N. S. ◽  
◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Algorithms

Enhanced Three-Dimensional Color Conversion Based on Discrete Wavelet Transform

2012 ◽  
Vol 155-156 ◽  
pp. 440-444
Author(s):  
He Yan ◽  
Xiu Feng Wang
Keyword(s):  
Information Technology ◽  
Wavelet Transform ◽  
Image Reconstruction ◽  
Image Quality ◽  
Image Compression ◽  
High Performance ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Compression Algorithms ◽  
Color Conversion

JPEG2000 algorithm has been developed based on the DWT techniques, which have shown how the results achieved in different areas in information technology can be applied to enhance the performance. Lossy image compression algorithms sacrifice perfect image reconstruction in favor of decreased storage requirements. Wavelets have become a popular technology for information redistribution for high-performance image compression algorithms. Lossy compression algorithms sacrifice perfect image reconstruction in favor of improved compression rates while minimizing image quality lossy.

scholarly journals The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

2018 ◽  
Vol 173 ◽  
pp. 03071
Author(s):  
Wu Wenbin ◽  
Yue Wu ◽  
Jintao Li
Keyword(s):  
Image Compression ◽  
Clustering Algorithm ◽  
Compression Algorithm ◽  
Prediction Algorithm ◽  
Two Dimensional ◽  
Spectral Image ◽  
Compression Algorithms ◽  
Serial Processing ◽  
Compression Time ◽  
Hyper Spectral

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

AN IMPROVED EMBEDDED ZEROTREE WAVELET IMAGE CODING METHOD BASED ON COEFFICIENT PARTITIONING USING MORPHOLOGICAL OPERATION

2000 ◽  
Vol 14 (06) ◽  
pp. 795-807 ◽  
Author(s):  
JUNMEI ZHONG ◽  
C. H. LEUNG ◽  
Y. Y. TANG
Keyword(s):  
Image Compression ◽  
Video Coding ◽  
Image Coding ◽  
Experimental Results ◽  
Morphological Operation ◽  
Compression Algorithms ◽  
Still Image ◽  
Coding Method ◽  
Wavelet Image ◽  
Image Compression Algorithm

In recent years, wavelets have attracted great attention in both still image compression and video coding, and several novel wavelet-based image compression algorithms have been developed so far, one of which is Shapiro's embedded zerotree wavelet (EZW) image compression algorithm. However, there are still some deficiencies in this algorithm. In this paper, after the analysis of the deficiency in EZW, a new algorithm based on quantized coefficient partitioning using morphological operation is proposed. Instead of encoding the coefficients in each subband line-by-line, regions in which most of the quantized coefficients are significant are extracted by morphological dilation and encoded first. This is followed by using zerotrees to encode the remaining space which has mostly zeros. Experimental results show that the proposed algorithm is not only superior to the EZW, but also compares favorably with the most efficient wavelet-based image compression algorithms reported so far.

FRACTAL IMAGE COMPRESSION ALGORITHMS BASED ON POSSIBILITY THEORY

Fractals ◽  
2007 ◽  
Vol 15 (02) ◽  
pp. 183-195 ◽  
Author(s):  
RUI YANG ◽  
XIAOYUAN YANG ◽  
B. LI
Keyword(s):  
Image Compression ◽  
Possibility Theory ◽  
Necessary Condition ◽  
Search Process ◽  
Average Intensity ◽  
Possibility Distribution ◽  
Image Block ◽  
Fractal Image Compression ◽  
Compression Algorithms ◽  
Fractal Image

Two fractal image compression algorithms based on possibility theory are originally presented in this paper. Fuzzy sets are used to represent the edge character of each image block, and two kinds of membership function are designed. A fuzzy integrated judgement model is also proposed. The model generates an accurate value for each edge block, which would be a label during the search process. The edge possibility distribution function and the edge necessity level are designed to control the quantity of the blocks to be searched. Meanwhile the pre-restriction is proposed, the average intensity value at different locations is used to be a necessary condition before the MSE computations. It is shown by our experiments that the encoding times of our two algorithms, compared to that of Jacquin's approach, are reduced to 60%–70% and 10%–20%, respectively.

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