Polishchuk U. Data compression using autoassociative neurolike structures.

Українська версія

Thesis for the degree of Candidate of Sciences (CSc)

State registration number

0411U001513

Applicant for

Specialization

  • 05.13.23 - Системи та засоби штучного інтелекту

15-02-2011

Specialized Academic Board

Д 35.052.14

Lviv Polytechnic National University

Essay

Thesis is dedicated to development of high-performance neural compression methods both without and with stepless loss regulation. In thesis the architecture of data compression system on the basis of neurolike geometrical transformation machine (GTM) structures in the autoassociative training mode with representation the neuron's output signals in a fixed-point number format is developed. Methods of additional increasing the effectiveness of neural data compression by the example of images are implemented. The two-step compression method by successive use the GTM network and lossless algorithms is development and approved for the first time. The perfected image compression method by division them into frames for GTM network, which provides additional increase the compression ratio, is developed and approved for the first time. The universality of developed neural data compression methods and means is verified by applying them for audio files.

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