In this paper, two multiple description coding schemes are developed, based on prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers, respectively. In both schemes, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. In the first method, due to predictive coding, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped. The optimal reconstruction is obtained by finding the intersection of all received bins. In the second method, joint dequantization is also used, but near-uniform offsets are created among different low-rate quantizers by quantizing the predictions and by employing unequal deadzones. By generalizing the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained for the first method, and a lower bound is obtained for the second method. The schemes are then applied to lapped transform-based multiple description image coding. The closed-form expressions enable the optimization of the lapped transform. An iterative algorithm is also developed to facilitate the optimization. Theoretical analyzes and image coding results show that both schemes achieve better performance than other methods in this category.

A multiple description coding scheme based on prediction-induced randomly offset quantizers is proposed, where each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. Due to the prediction, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped with each others. The optimal reconstruction is obtained by finding the intersection of all received quantization bins. Using the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained. The proposed scheme is then applied to lapped transform-based multiple-description image coding, and an iterative optimization scheme is developed to find the optimal lapped transform. Experimental results show that the proposed scheme achieves better performance than other methods in this category.

In this paper, a three-layer scheme is developed for M-channel multiple description image coding. In each description, a subset of the source samples is encoded in the first layer. In the second layer, the remaining subsets are encoded sequentially by predicting from the already encoded subsets. The third layer encoding is designed to refine the reconstruction when only one description is lost, which is the dominant loss scenario in practice. We first derive the closed-form expressions of the expected distortion of the system for 1-D sources when different numbers of descriptions are received. The scheme is then applied to lapped transform based image coding. Simulation results show that the method outperforms some competing schemes.

A low complexityM-channel multiple description coding scheme is developed in this paper, in which each description carries one subset of the input with a higher bit rate and the rest with a lower bit rate. The lower-rate codings in different descriptions are designed to be mutually refinable using staggered scalar quantizers. For correlated sources, a two-rate predictive coding is used in each description. Closed-form expressions of the distortions are derived when different numbers of descriptions are received. The application of the proposed scheme in lapped transform based image coding is also investigated, and the optimal transform is obtained. Experimental results using both 1-D memoryless sources and 2-D images demonstrate the superior performance of the proposed scheme.

A new multiple description coding paradigm is proposed by combining the time-domain lapped transform, block level source splitting, linear prediction, and prediction residual encoding. The method provides effective redundancy control and fully utilizes the source correlation. The joint optimization of all system components and the asymptotic performance analysis are presented. Image coding results demonstrate the superior performance of the proposed method, especially at low redundancies.

A low complexity multiple description (MD) coding method is proposed to generate M descriptions. Consider the MD coding of a stationary correlated source. We first fictitiously partition the source into sample blocks of size M, i.e., M polyphases. Each description encodes all input samples, but with a variable bit rate that depends on the indices of the sample and the description. A special DPCM encoder is used in each description, where each sample is predicted from the reconstructed samples in the same description. The prediction error is uniformly scalar-quantized and entropy coded.

This paper presents a new low complexity multiple description coding (MDC) method that can generate any number of descriptions. For correlated sources, a special DPCM encoder is used in each description, such that it carries higher rate information of a subset of the samples and lower rate information of the rest. The lower rate codings in different descriptions are designed to be mutually refinable using staggered scalar quantizers. The closed-form expression of the expected distortion is derived when an arbitrary subset of the descriptions are received. Experimental results on natural images using lapped transform show that the proposed method is competent with the state-of-the-art multiple description image coders.

A new multiple description image coding paradigm is presented in this paper by combining the lapped transform, block level source splitting, inter-description prediction, and coding of the prediction residual. Jointly optimal designs of all system components are discussed. Compared with the best multiple description image coding algorithm in the literature, the new method can achieve significant improvement when one description is lost, given the same bit rate and the same central distortion.

The scenario considered in this paper is the transmission of a continuous information source over a set of erasure channels, by using a multiple description quantizer to deal with channel erasures and a convolutional channel code on each channel to deal with random bit errors. The diversity available in multiple descriptions is subsequently exploited in Viterbi sequence detectors to jointly decode the convolutional codes. Two approaches to joint decoding are presented and investigated. Simulation results are presented for two-channel multiple description quantization of Gaussian sources which demonstrate the potential improvements in end-to-end source distortion achievable with joint decoding of channel codes in a multiple description system. We also compare the performance of joint Viterbi detectors with that of turbo-style iterative decoding of multiple-description codes proposed earlier.