We cascade an existing image coder with carefully chosen error control coding, and thus produce a progressive image compression scheme whose performance on a noisy channel is significantly better than that of previously known techniques. The main idea is to trade off the available transmission rate between source coding and channel coding in an efficient manner. This coding system is easy to implement and has acceptably low complexity. Furthermore, effectively no degradation due to channel noise can be detected; instead, the penalty paid due to channel noise is a reduction in source coding resolution. Detailed numerical comparisons are given that can serve as benchmarks for comparisons with future encoding schemes. For example, for the 512 512 Lena image, at a transmission rate of 1 b/pixel, and for binary symmetric channels with bit error probabilities 03 , 02 , and 01 , the proposed system outperforms previously reported results by at least 2.6, 2.8, and 8.9 dB, respectively.

We extend the work of Sherwood and Zeger [1, 2] to progressive video coding for noisy channels. By utilizing a three-dimensional (3-D) extension of the set partitioning in hierarchical trees (SPIHT) algorithm [3], we cascade the resulting 3-D SPIHT video coder [4, 5] with the ratecompatible punctured convolutional (RCPC) channel coder [6] for transmission of video over a binary symmetric channel (BSC). Progressive coding is achieved by increasing the target rate of the 3-D embedded SPIHT video coder as the channel condition improves. The performance of our proposed coding system is acceptable at low transmission rate and bad channel conditions. 1 Introduction Transmission of compressed images/video over a noisy channel may su#er from disturbance or channel noise. The result is usually uncontrolled degradation in reproduction quality, especially when variable-length coding (VLC) is used for high performance compression. Therefore, a major concern of the designer is the control o...

A progressive method for transmission of images over a bursty noise channel is presented. It is based on discrete wavelet transform (DWT) coding and channel-optimized scalar quantization. The main advantage of the proposed system is that it exploits the channel memory and hence has superior performance over a similar scheme designed for the equivalent memoryless channel through the use of channel interleaving. In fact, the performance of the proposed system improves as the noise becomes more correlated, at a xed bit error rate. Comparisons are made with other alternatives which employ independent source and channel coding over the fully interleaved channel at various bit rates and bit error rates. It is shown that the proposed method outperforms these substantially more complex systems for the whole range of considered bit rates and for a wide range of channel conditions.

We present a new progressive method for image transmission over binary channels with additive bursty noise. The method is based on transform coding, subband modeling, and channel-optimized scalar quantization (COSQ). It requires a negligible amount of side information (less than 0.00009 bpp) and oers superior performance over a similar system designed for the fully interleaved channel, due to the exploitation of channel memory. Increased correlation among channel noise samples leads to a better performance in our system. Comparisons are made with a competing system which employs separate source and channel coding over the fully interleaved channel and uses adaptive bit allocation between the two codes. Our proposed method outperforms this substantially more complex system for the whole range of considered bit rates and a wide range of channel conditions.

Channel optimized vector quantization (COVQ), as a joint source-channel coding scheme, has proven to perform well in compressing a source and making the resulting quantizer codebook robust to channel noise. Unfortunately like its counterpart in the noiseless channel case, the vector quantizer (VQ), the COVQ encoding complexity is inherently high. Sample adaptive product quantization was recently introduced by Kim and Shro to reduce the complexity of the VQ while achieving comparable distortions, even for moderate quantization dimensions. In this thesis, we investigate the SAPQ for the case of noisy memoryless channels and employ the joint sourcechannel approach of optimizing the quantizer design by taking into account both source and channel statistics. It is shown that, like its counterpart in the noiseless case, the channel optimized SAPQ achieves comparable performance results to the COVQ (within 0.2-0.8 dB), while maintaining considerably lower encoding complexity (half of that of COVQ) and storage requirements.