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Subband coding and image compression using CNN
Author(s) -
MoreiraTamayo Oscar,
Pineda De Gyvez José
Publication year - 1999
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/(sici)1097-007x(199901/02)27:1<135::aid-cta45>3.0.co;2-a
Subject(s) - image compression , computer science , lossy compression , decorrelation , data compression , convolutional neural network , quantization (signal processing) , lossless compression , artificial intelligence , algorithm , texture compression , theoretical computer science , image processing , image (mathematics)
The cellular neural network paradigm has found many applications in image processing. However, algorithms for image compression using CNN have scarcely been explored. CNN programmability is based on a new algorithmic style based on the spatio‐temporal properties of the array. By exploiting the massive parallelism provided by CNN and the convolutional key basic instruction, a fast and efficient compression process can be achieved. This paper presents new templates and low‐complexity algorithms to perform both the linear and non‐linear operations needed for image compression. In this work, we have addressed all the transformation steps needed in image compression, i.e. decorrelation, bit allocation, quantization and bit extraction. From all possible compression techniques the wavelet subband coding was chosen because it is considered one of the most successful techniques for lossy compression. It allows a high compression ratio while preserving the image quality. All these advantages are implemented in the algorithms hereby presented. Copyright © 1999 John Wiley & Sons, Ltd.