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Image compression based on low-pass wavelet transform and multi-scale edge compensation. Part II: MSEC model

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1 Author(s)
Xiaohui Xue ; Dept. of Comput. Sci., Harbin Inst. of Technol., China

Summary form only given. This paper presents the idea of multi-scale edge compensation, and puts forward an image compression method (MSEC) based on the low-pass wavelet transform and multi-scale edge compensation. The encoder performs edge detection, edge compensation at every scale from fine to coarse, outputs the model information and the final background. The decoder synthesizes the image according to the recorded information of the multi-scale edge model and the background. Experimental results are considerably encouraging. For 512×512×24 bits Lena, when compressed by 159 times, the PSNR values for Y, U and V components are 28.2 dB, 34.6 dB and 34.5 dB respectively. For a large class of images, compression as high as about 500 times is achieved, and the image quality remains acceptable. As a matter of fact, the performance of current MSEC system can be greatly improved in the future since the MSEC technique involves many aspects of image processing including both image analysis and realistic image generation. The theory of the MSEC model consists of two components. One is the model and processing methods for the edges of MSEC. Scalability and recognition ability of the edge detection are essential to MSEC. MSEC recognizes and processes two different kinds of edges: roof edge and step edge. The scalability of an edge is associated with the algebraic precision of the low-pass wavelet. The compensation models of edge profile and edge shape are also new concepts of the MSEC. The other is the low-pass wavelet transform of the MSEC, which studies the properties of the low-pass wavelet transform in detail and explains why we use the low-pass wavelet as our muti-scale transform tool. The concept of algebraic precision of the low-pass wavelet transform is crucial. The frequency response of the low-pass wavelet is also inspected

Published in:

Data Compression Conference, 1999. Proceedings. DCC '99

Date of Conference:

29-31 Mar 1999

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