The CA-CMAC for downsampling image data size in the compressive domain

Author

Tao, Ted ; Lu, Hung-Ching ; Hung, Ta-Hsiung

Author_Institution

Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan

Volume

5

fYear

2002

fDate

6-9 Oct. 2002

Abstract

The CA-CMAC for downsampling image data size in the compressive domain is proposed in this paper. When the transmitting data is limited, it can reduce the bit rate during transmitting image data and decrease computations per pixel during the reconstructive process. The proposed method maps the image data into the CMAC lookup table, which can learn the characteristics of original image and can change image data size during downsampling and upsampling processes. It is unlike the conventional linear interpolation method, which gets lower SNR and costs more computation in the compression and reconstructive processes. The CA-CMAC method uses only a few hypercubes to learn the characteristics of original image, and transmits the learned characteristics to the receiver for reconstruction. Finally, the proposed method is applied to downsample JPEG data size in this paper, and it is shown that it gets high SNR after reconstruction.

Keywords

cerebellar model arithmetic computers; hypercube networks; image coding; image reconstruction; learning (artificial intelligence); sensitivity analysis; CA-CMAC; JPEG data size; bit rate; compressive domain; hypercubes; image data size downsampling; learned characteristics; receiver; reconstructive process; upsampling; Bit rate; Computational efficiency; Convergence; Data communication; Hypercubes; Image coding; Image reconstruction; Interpolation; Pixel; Table lookup;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2002 IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-7437-1

Type

conf

DOI

10.1109/ICSMC.2002.1176424

Filename

1176424