We combine multiple description (MD) quantization, entropy coding, and data partitioning to improve the error resiliency of images over varying packet loss channels. Our proposed scheme degrades gracefully as channel conditions worsen. A multidimensional extension of the two-channel multiple description scalar quantizer (MDSQ) improves robustness. A high performance wavelet image coder is designed for MDSQ, using the set-partitioning-in-hierarchical-trees (SPIHT) algorithm for entropy coding. Experimentally, with 4 descriptions, the new coder outperforms previous reports at any loss rate.

One method of transmitting wavelet based zerotree encoded images over noisy channels is to add channel coding without altering the source coder. A second method is to reorder the embedded zerotree bitstream into packets containing a small set of wavelet coefficient trees. We consider a hybrid mixture of these two approaches and demonstrate situations in which the hybrid image coder can outperform either of the two building block methods, namely on channels that can suffer packet losses as well as statistically varying bit errors.

We investigate the problem of selecting blocklength and code rate for progressive image transmission, motivated by turbo coding methods where the performance improves with blocklength. The problem is to balance the tradeoff among error protection, source coding rate, and delay. We propose a general performance measure for evaluating progressive transmission and use dynamic programming to determine the channel code parameters based on the progressive performance. Performance results are provided for the evaluation of the gains over less complex methods as a function of channel error rate.

A product channel code is proposed to protect progressively compressed and packetized image data that is transmitted across noisy channels. Across packets, the product code is composed of Reed-Solomon codes. Within packets, the product code uses the concatenation of a rate compatible punctured convolutional code and an error detecting parity check code. The benefits include flexibility in terms of delay, the ability to easily adapt the level of protection based on importance (i.e., unequal error protection), and scalable decoding complexity. The system outperforms the best known image coders for memoryless channels and performs well on fading channels.

Product channel codes are proposed to protect progressively compressed and packetized images for noisy channels. Within packets, the product code uses the concatenation of a rate-compatible punctured convolutional code and an error detecting parity check code. Across packets, Reed-Solomon codes are used. Benefits include flexible choice of delay, adaptability of error protection level (i.e., unequal error protection), and scalable decoding complexity. The system outperforms the best known image coders for memoryless channels and performs well on fading channels.

Numerous sophisticated techniques have been developed over the last several decades to efficiently transmit images across noisy channels. Here, 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 image compression 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. As an example, for the 512/spl times/512 Lena image, at an overall transmission rate of 1 bit per pixel, and for binary symmetric channels with bit error probabilities 10/sup -3/, 10/sup -2/, and 10/sup -1/, the proposed system typically outperforms other existing systems by at least 2.6 dB, 2.8 dB, and 8.9 dB, respectively.

We present a wavelet-based progressive image coding system that allows the specification of regions of interest to alter the spatial allocation of future transmitted bits. When used in an interactive mode with feedback, the viewer, after seeing a low resolution version of the image, can instruct the encoder to concentrate more effort on coding regions of interest. When operating in an independent mode, the encoder can select regions of interest based on automatic feature recognition algorithms or offline human selection. The algorithm is flexible enough to adapt to changes of interest during encoding.

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/spl times/512 Lena image, at a transmission rate of 1 b/pixel, and for binary symmetric channels with bit error probabilities 10/sup -3/, 10/sup -2/, and 10/sup -1/, the proposed system outperforms previously reported results by at least 2.6, 2.8, and 8.9 dB, respectively.