Abstract This paper describes a hardware design for a real-time lossless image compression system based on the organized matrix scanning methodology, SCAN. SCAN is a special purpose context-free language which describes and generates a wide range of array accessing algorithms from a short set of simple ones. These algorithms may represent scan techniques for image processing, but at the same time they stand as generic data accessing strategies. In this system, an 8-bit gray scale image is transformed into an error image using differential pulse code modulation (DPCM). This error image is subdivided into 64×64 pixel blocks, and the bit planes associated with each block are scanned with four different scan sequences. For each image block and each bit plane, the results of the scan which most closely matches the grain of the image is Huffman coded for transmission. High data rates associated with the real-time requirement make a software solution impossible. Pipelining is employed to simultaneously perform the functions of image input, DPCM, SCAN based run-length encoding, Huffman coding and output. Parallel processing is utilized to perform the scanning and run-length encoding on all bit planes simultaneously. In addition to compression, the use of the SCAN methodology provides good data encryption, and can be extended to include data hiding or watermarking.
[1]
Frederick J. Hill,et al.
Digital systems: hardware organization and design
,
1973
.
[2]
Nikolaos G. Bourbakis,et al.
Scan image compression-encryption hardware system
,
1995,
Electronic Imaging.
[3]
Nikolaos G. Bourbakis,et al.
A Parallel Implementation of the Scan Language
,
1989,
Comput. Lang..
[4]
William A. Pearlman,et al.
An image multiresolution representation for lossless and lossy compression
,
1996,
IEEE Trans. Image Process..
[5]
Nikolaos G. Bourbakis,et al.
Image encryption method using a class of fractals
,
1995,
J. Electronic Imaging.
[6]
Jorma Rissanen,et al.
Applications of universal context modeling to lossless compression of gray-scale images
,
1995,
Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.
[7]
N. Ranganathan,et al.
A lossless image compression algorithm using variable block size segmentation
,
1995,
IEEE Trans. Image Process..