Data analysis for image transmitted using Discrete Wavelet Transform and Vector Quantization compression

In this paper we are going to study the effect of channel noise in image compressed with vector quantization and discrete wavelet transform. The objective of this study is to analyze and understand the way that the noise attack transmitted data by doing lot of tests like dividing the indices in different levels according to discrete wavelet transform and dividing  each level in frames of bits. The collected information well helps us to propose solutions to make the received image more resistible to the channel noise also to benefit from the good representation obtained by using vector quantization and discrete wavelet transform

The Noise Reduction over Wireless Channel Using Vector Quantization Compression and Filtering

The transmission of compressed data over wireless channel conditions represents a big challenge. The idea of providing robust transmission gets a lot of attention in field of research. In this paper we study the effect of the noise over wireless channel. We use the model of Gilbert-Elliot to represent the channel. The parameters of the model are selected to represent three cases of channel. As data for transmission we use images in gray level size 512x512. To minimize bandwidth usage we compressed the image with vector quantization also in this compression technique we study the effect of the codebook in the robustness of transmission so we use different algorithms to generate the codebook for the vector quantization finally we study the restoration efficiency of received image using filtering and indices recovery technique

Interleaved reception method for restored vector quantization image

The transmission of image compression by vector quantization produce wrong blocks in received image which are completely different to the original one that makes the restoration process too hard because we don’t have any information about the original blocks. As a solution of this problem we try to keep the maximum of pixels that form the original block by building new blocks. Our proposition is based on decomposition and interleaving. For the simulation we use a binary symmetric channel with different BER and in the restoration process we use simple median filter just to check the efficiency of proposed approach