Abstract
It is difficult for the conventional image compression method to achieve good compression effect in the underwater acoustic image (UWAI), because the UWAI has large amount of noise and low correlation between pixel points. In this paper, fractal coding is introduced into UWAI compression, and a fractal coding algorithm based on interest region is proposed according to the importance of different regions in the image. The application problems of traditional quadtree segmentation in UWAIs was solved by the range block segmentation method in the coding process which segmented the interest region into small size and the noninterest region into large size and balanced the compression ratio and the decoded image quality. This paper applies the classification, reduction codebook, and correlation coefficient matching strategy to narrow the search range of the range block in order to solve the problem of the long encoding time and the calculation amount of encoding process is greatly reduced. The experimental results show that the proposed algorithm improves the compression ratio and encoding speed while ensuring the image quality of important regions in the UWAI.
Highlights
The underwater acoustic image (UWAI) refers to an image generated by imaging sonar according to the characteristics of the underwater target echo signal by means of acoustic wave detection
In order to verify the effectiveness of the algorithm, two UWAIs: ship and plane were used for the simulation experiment
In view of the particularity of UWAIs, this paper uses fractal coding based on partial similarity to compress the UAWI
Summary
The UWAI refers to an image generated by imaging sonar according to the characteristics of the underwater target echo signal by means of acoustic wave detection. In order to solve the long fractal coding time, scholars have proposed several methods such as classification matching [7,8,9,10], optimized search range, and nonsearch coding [11,12,13,14,15]; scholars propose a variety of different range block partitioning schemes, such as quadtree partitioning [16,17,18], HV segmentation [19], triangulation, and irregular segmentation [20, 21] to balance the problem of compression ratio and decoded image quality The latter three methods require more information to describe the position and shape of each subblock compared with quadtree partitioning, and the search matching processes are more complicated. The structure of this paper is as follows: the second section briefly introduces the principle of basic fractal coding; the third section focuses on the fractal coding algorithm based on region of interest and the correlation coefficient fractal coding algorithm is proposed; the simulation and experiment results are given in the fourth section; the fifth section comes the conclusion
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