Subband coding and image compression using CNN

O. Moreira-Tamayo, J. Pineda de Gyvez

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)135-151
Number of pages17
JournalInternational Journal of Circuit Theory and Applications
Volume27
Issue number1
DOIs
Publication statusPublished - 1999

Fingerprint

Dive into the research topics of 'Subband coding and image compression using CNN'. Together they form a unique fingerprint.

Cite this