JOINT SOURCE-CHANNEL CODING USING TREE-STRCTURED VECTOR QUANTIZATION FOR REMOTE SENSING IMAGES / CODIFICAÇÃO CONJUNTA, PARA FONTE E CANAL, USANDO QUANTIZAÇÃO VETORIAL ESTRUTURADA EM ÁRVORE, PARA IMAGENS DE SENSORIAMENTO REMOTO
AUTOR(ES)
RAFAEL DONNICI DE AZEVEDO
DATA DE PUBLICAÇÃO
2000
RESUMO
This work studies the problem of remote sensorng image compression by joint source-channel coding. The vector quantizer methods evaluated are those designed with the LBG algorithm, the COVQ (channel-optimized vector quantizer) algorithm as well as tree-structured vector quantizer. The noisy channel is modelled as a BSC. In this context, two news methods are proposed: (1) A tree- structures vector quantizer that considers the transmission through noisy channels (denominated CD-TSVQ), and (2) a new class of compressors that uses forward error- correcting codes over the TSVQ structure, as a way to actively protect data during the transmission. The twoproposed methods can be combined on the same compressor architecture, resulting in a vast class of compressors well-adapted to the transmission through noisy channels. Results allowing the comparision of the proposed methods with existing ones are presented. Performance evaluated in a scenery where images are compressed to be transmited at a rate of 1.5bpp. Results yield to the conclusion that the porposed methods are much less complex than the existing methods, yet achieve equivalent or, in some situations, improved performance.
ASSUNTO(S)
codificacao conjunta para fonte e canal vector quantisation quantizacao vetorial joint source-channel coding compressao de imagens image compression
