Wenqing Jiang

Multiple description coding via polyphase transform and selective quantization

In this paper, we present an efficient Multiple Description Coding (MDC) technique to achieve robust communication over unreliable channels such as a lossy packet network. We first model such unreliable channels as erasure channels and then we present a MDC system using polyphase transform and selective quantization to recover channel erasures. Different from previous MDC work, our system explicitly separates description generation and redundancy addition which greatly reduces the implementation complexity specially for systems with more than two descriptions. Our system also realizes a Balanced Multiple Description Coding (BMDC) framework which can generate descriptions of statistically equal rate and importance. This property is well matched to communication systems with no priority mechanisms for data delivery, such as todays Internet. We then study, for a given total coding rate, the problem of optimal bit allocation between source coding and redundancy coding to achieve the minimum average distortion for different channel failure rates. With high resolution quantization assumption, we give optimal redundancy bit rate allocations for both scalar i.i.d sources and vector i.i.d sources for independent channel failures. To evaluate the performance of our system, we provide an image coding application with two descriptions and our simulation results are better than the best MDC image coding results reported to date. We also provide image coding examples with 16 descriptions to illustrate the simplicity and effectiveness of our proposed MDC system.

Lifting factorization-based discrete wavelet transform architecture design

In this paper, two new system architectures, overlap-state sequential and split-and-merge parallel, are proposed based on a novel boundary postprocessing technique for the computation of the discrete wavelet transform (DWT). The basic idea is to introduce multilevel partial computations for samples near data boundaries based on a finite state machine model of the DWT derived from the lifting scheme. The key observation is that these partially computed (lifted) results can also be stored back to their original locations and the transform can be continued anytime later as long as these partial computed results are preserved. It is shown that such an extension of the in-place calculation feature of the original lifting algorithm greatly helps to reduce the extra buffer and communication overheads, in sequential and parallel system implementations, respectively. Performance analysis and experimental results show that, for the Daubechies (see J.Fourier Anal. Appl., vol.4, no.3, p.247-69, 1998) (9,7) wavelet filters, using the proposed boundary postprocessing technique, the minimal required buffer size in the line-based sequential DWT algorithm is 40% less than the best available approach. In the parallel DWT algorithm we show 30% faster performance than existing approaches.

Multiple description speech coding for robust communication over lossy packet networks

Robust speech communication over unreliable channels is one of the key research areas in the development of voice-over IP (VoIP) technology. The authors propose a multiple description coding (MDC) based speech packetization scheme to combat packet losses. The basic idea is to encode each input speech frame into multiple packets, each of which can be independently decoded. Explicit redundancy is added such that each packet can render an acceptable signal reconstruction of the original frame. Unlike previous approaches using explicit redundancy for loss recovery (D.E. Tsoukalas et al., 1997), we propose to improve the redundancy coding efficiency using context adaptive techniques. Simulation results on independent packet losses show that the proposed scheme gives better average reconstruction audio quality at low loss rates (/spl les/20%) compared to that of previous works.

Parallel architecture for the discrete wavelet transform based on the lifting factorization

One major difficulty in designing an architecture for the parallel implementation of Discrete Wavelet Transform (DWT) is that the DWT is not a block transform. As a result, frequent communication has to be set up between processors to exchange data so that correct boundary wavelet coefficients can be computed. The significant communication overhead thus hampers the improvement of the efficiency of parallel systems, specially for processor networks with large communication latencies. In this paper we propose a new technique, called Boundary Postprocessing, that allows the correct transform of boundary samples. The basic idea is to model the DWT as a Finite State Machine based on the lifting factorization of the wavelet filterbanks. Application of this technique leads to a new parallel DWT architecture. Split-and-Merge, which requires data to be communicated only once between neighboring processors for any arbitrary level of wavelet decompositions. Example designs and performance analysis for 1D and 2D DWT show that the proposed technique can greatly reduce the interprocessor communication overhead. As an example, in a two-processor case our proposed approach shows an average speedup of about 30% as compared to best currently available parallel computation.

Multiple description coding via scaling-rotation transform

We propose a two-stage transform design technique for multiple description transform coding. The first stage is the structure design in which we enforce a scaling-rotation factorization of the transform and we further constrain the transform for specific channel conditions using the knowledge of the input correlation matrix and the desired output correlation matrix. In the second stage, magnitude design, we find the optimal transform from all admissible transforms given by the structure design using the numerical algorithm proposed by Goyal et al. (see Proc. of IEEE Data Compression Conference, 1998). Such a design enables a structured transform framework which reduces both the design and implementation complexities compared to an exhaustive search through the whole space of nonorthogonal transforms. We give two examples to illustrate the design idea, the scaling-Hadamard transform for equal rate channels and the scaling-DST transform for sequential protection channels.

Implementations of the discrete wavelet transform: complexity, memory, and parallelization issues

The discrete wavelet transform (DWT) has been touted as a very effective tool in many signal processing application, including compression, denoising and modulation. For example, the forthcoming JPEG 2000 image compression standard will be based on the DWT. However, in order for the DWT to achieve the popularity of other more established techniques (e.g., the DCT in compression) a substantial effort is necessary in order to solve some of the related implementation issues. Specific issues of interest include memory utilization, computation complexity and scalability. In this paper we concentrate on wavelet-based image compression and provide examples, based on our recent work, of how these implementation issues can be addressed in three different environments, namely, memory constrained applications, software-only encoding/decoding, and parallel computing engines. Specifically we will discuss (1) a low memory image coding algorithm that employs a line-based transform, (2) a technique to exploit the sparseness of non- zero wavelet coefficients in a software-only image decoder, and (3) parallel implementation techniques that take full advantage of lifting filterbank factorizations.

Efficient discrete wavelet transform architectures based on filterbank factorizations

In this paper, a boundary postprocessing technique is proposed to compute the discrete wavelet transform (DWT) near block boundaries. The basic idea is to take advantage of available lifting filterbank factorizations to model the DWT as a Finite State Machine (FSM). The proposed technique can reduce the size of auxiliary buffers in block-based DWT implementations and reduce the communication overhead between adjacent blocks. Two new DWT system architectures, Overlap-State sequential and Split-and-Merge parallel, are presented using this technique. Experimental results show that, for the popular (9, 7) filters, the size of auxiliary buffers can be reduced by 42% and that the parallel algorithm is 30% faster than existing approaches.

Forward/backward adaptive context selection with applications to motion vector field encoding

In low rate motion-compensated video coding, the rate required to encode the motion field can become a significant portion of the overall rate budget. This motivates us to investigate methods to efficiently, and losslessly, encode the motion field. Reductions in the required motion field bit rate allow increasing the rate for the residue images or may permit a higher density motion field to be used. We consider adaptive context modeling techniques, such as those proposed in image coding applications, and explore their effectiveness for coding the motion data. We rely on various forward/backward context selection algorithms, and demonstrate how forward adaptation, based on alphabet partitioning approaches, can result in performance improvements over purely backward adaptive methods. We observe substantial reductions in rate, especially for dense motion fields, when comparing with popular differential coding schemes using VLC tables, such as those in the H.263 standard.

A novel packet loss recovery technique for multimedia communication

In this paper a novel loss recovery technique is proposed for multimedia communications over lossy packet networks. The proposed technique uses a combination of recent results on multiple description coding and erasure recovery codes in channel coding. The uniqueness of the proposed technique lies in its ability to recover not only the data carried in lost packets, but also the decoding state for successive packets. Experimental results on image and speech coding show that the proposed technique has excellent coding performance compared to some of the best results published and it can also significantly reduce the error propagation in successive packets due to packet losses.

Backward context adaptive interframe coding

This paper presents a backward context adaptive coder for motion-compensated difference frames based on a mixture density model. In this model, each pixel is assumed to be generated from a random source with probability distribution conditioned on the interframe context which consists of the local intensity context and the local displacement vector context. To estimate this conditional probability distribution, a backward adaptive nonparametric approach is chosen in our work due to its fast adaptivity to the input data characteristics. Once this probability distribution is found, each pixel can then be coded by using the corresponding entropy coder. As an application, we modified Telenors TMN5 H.263 algorithm based on this context adaptive coding idea and implemented a hybrid interframe coder. Our simulation results show that the coding performance at 8kbps is comparable to that of H.263 without obvious blocking effects and our coder can also be implemented with lower complexity.