A Novel Sinusoidal Speech Codec Using Multiple Descriptions

Robust and flexible speech codecs are required more and more by speech communication over unreliable and heterogeneous channels such as the Internet. In this paper, a novel multiple description scalable codec is proposed, which is based on the sinusoidal model and equivalent rectangular bandwidth (ERB) noise model. It combines the advantages of scalable coding and multiple descriptions coding (MDC), and provides relatively high transmission reliability and rate adaptation. The lost packet does not affect the state recovery of this state-less codec. The performance of the proposed codec was evaluated under memory and memoryless channels. Analytical and experimental results show that it is suitable to unreliable and band limited channel such as Internet.

A multiple description scalar quantization (MDSQ) based coding system can be regarded as a source coder (quantizer) followed by a channel coder, i.e. the combination of index and codeword assignment. The redundancy, or the correlation between the descriptions, is controlled by the number of diagonals covered by the index assignment. We consider here the usage of multiple description uniform scalar quantization (that we call MDUSQ) for robust and progressive transmission of images over unreliable channels. The progressive feature is an important factor for rate control in non-stationary (varying bandwidth) communication environments. In this context, the paper describes an embedded index assignment strategy that provides improved rate-distortion performances in progressive transmission scenarios, against index assignments defined so far for MDSQ. The MDUSQ together with the embedded index assignment algorithm are incorporated into the JPEG2000 verification model. The approach is compared against a progressive multiple description scheme based on a polyphase transform (PT) decomposition of the signal.

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

The multiple description (MD) problem has received considerable attention as a model of information transmission over unreliable channels. A general framework for designing efficient multiple description quantization schemes is proposed in this paper. We provide a systematic treatment of the El Gamal-Cover (EGC) achievable MD rate-distortion region, and show that any point in the EGC region can be achieved via a successive quantization scheme along with quantization splitting. For the quadratic Gaussian case, the proposed scheme has an intrinsic connection with the Gram-Schmidt orthogonalization, which implies that the whole Gaussian MD rate-distortion region is achievable with a sequential dithered lattice-based quantization scheme as the dimension of the (optimal) lattice quantizers becomes large. Moreover, this scheme is shown to be universal for all i.i.d. smooth sources with performance no worse than that for an i.i.d. Gaussian source with the same variance and asymptotically optimal at high resolution. A class of low-complexity MD scalar quantizers in the proposed general framework also is constructed and is illustrated geometrically; the performance is analyzed in the high resolution regime, which exhibits a noticeable improvement over the existing MD scalar quantization schemes.

Multiple description (MD) coding is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. Furthermore, practical variable-bandwidth channels also require fine grain scalability of the descriptions (bit streams). In this paper, according to the geometrical structure and the special relationship of lattice vector quantizers, a MD quantizer called shifted lattice vector quantization (SLVQ) is employed in MD image coding to realize progressive transmission over unreliable channels. In view of the characteristics of wavelet coefficients in different frequency subbands, besides an appropriate construction of wavelet coefficient vectors, the algorithm of modified zerotree coding is also applied to improve compression performance. Experimental results validate the effectiveness of the proposed scheme with better performance than the other schemes based on MD scalar quantization for progressive transmission.

Multiple Description Coding (MDC) is a method to solve the problem of noisy channels affecting images. Multiple descriptions (multiple copies) of images are transmitted over different channels while at the receiver; the images are reconstructed using the different copies. In this paper, five MDC methods are proposed and discussed, while results shown their improvement in images quality.

Robust progressive image transmission over unreliable channels with variable bandwidth requires multiple description coding (MDC) systems that produce highly error-resilient embedded bit-streams. The proposed embedded multiple description scalar quantizers (EMDSQ) meet the desired features consisting of a high redundancy level, fine grain rate adaptation and progressive transmission of each description. Experimental results show that EMDSQ yield better rate-distortion performance in comparison to the multiple description uniform scalar quantizers (MDUSQ) previously proposed in the literature. Moreover, the generalized form of EMDSQ targeting an arbitrary number of channels is proposed, which offers the possibility of designing realistic coders for practical multi-channel communication systems.

The compressed sensing paradigm allows to efficiently represent sparse signals by means of their linear measurements. However, the problem of transmitting these measurements to a receiver over a channel potentially prone to packet losses has received little attention so far. In this paper, we propose novel methods to generate multiple descriptions from compressed sensing measurements to increase the robustness over unreliable channels. In particular, we exploit the democracy property of compressive measurements to generate descriptions in a simple manner by partitioning the measurement vector and properly allocating bit-rate, outperforming classical methods like the multiple description scalar quantizer. In addition, we propose a modified version of the Basis Pursuit Denoising recovery procedure that is specifically tailored to the proposed methods. Experimental results show significant performance gains with respect to existing methods.

Multiple Description Coding has recently proved to be an effective solution for the robust transmission of 3D video sequences over unreliable channels. However, adapting the characteristics of the source coding strategy (Cognitive Source Coding) permits improving the quality of 3D visualization experienced by the end-user. This strategy has been successfully employed for standard video signals, but it can be applied to Multiple Description video coding for an effective transmission of 3D signals. The chapter presents a novel Cognitive Source Coding scheme that improves the performance of traditional Multiple Description Coding approaches by adaptively combining traditional predictive and Wyner-Ziv coders according to the characteristics of the video sequence and to the channel conditions. The approach is employed for video+depth 3D transmissions improving the average PSNR value up to 2.5 dB with respect to traditional MDC schemes.KeywordsMultiple description3DTV transmissionDistributed video codingCognitive source codingDIBR videoRobust video coding

In this paper, we propose a scheme for the robust transmission of video in error prone environments using multiple description coding (MDC) based on the scalable video coding extension (SVC) of H.264/AVC. Due to the layer structure of SVC, a base layer is referenced by one or more enhancement layers. The proposed method produces multiple description base layers to achieve robust video communication over unreliable channels with reasonable redundancy. Two base layers are generated using residual data downsampling, which makes it possible that the two base layers have the same motion vectors. The proposed method combines the advantages of SVC and MDC. Experimental results show that the proposed algorithm outperforms the temporal splitting based multiple description scalable coding method in terms of PSNR by 0.3 dB.

We compare different source diversity methods for converstional voice communication over multiple routes in a mobile ad-hoc network (MANET). A new multiple description (MD) codec based on the AMR-WB codec, with two balanced side descriptions (6.9 kbps each) is presented. We compare the performance of the MD codec against two other diversity methods, 1) duplicating speech encoded with AMR-WB at 6.6 kbps and 2) duplicating speech encoded with AMR-WB at 12.65 kbps. We show that because of the large packet headers added to each packet by typical MANET protocols, the overhead of sending the simple path diversity methods is not much larger than the overhead for sending MD streams over different paths, and the gain in speech quality we get from duplicating AMR-WB at 12.65 kbps over sending MD codec streams is significant. We compare the speech quality delivered by each of the methods under random and bursty packet loss conditions. The quality of decoded speech is evaluated using WPESQ, a wideband extension to the PESQ algorithm.

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.

In this paper a scalable multiple description video coding approach based on embedded multiple description scalar quantization (EMDSQ) is presented. The proposed approach enables the progressive transmission of video over unreliable channels with variable bandwidth. Experimental results show that in lossy transmission conditions the proposed embedded multiple description coding system yields better rate-distortion performance compared to single description video coding and can efficiently sustain 20% of losses.

Communications in general require protection due to error-prone channels. In geoscience and remote sensing, especially coded or compressed data, and results from classifications are vulnerable to transmissions errors. Multiple descriptions of data are one way for protection of communications over unreliable channels. This study concentrates on multiple description of spectral images as a way for providing scalable coding. The principal component analysis outputs the common content, the redundant part, for the two descriptions and then the integer wavelet transform selects different contents for those descriptions. In the experiments, the goal was to find good parameterization in the transmitter for generating the two descriptions which will allow perfect reconstruction if both of them are available at the receiver. The reconstruction quality for various number of principal components is demonstrated. Integer wavelet filter 5/3 showed the best performance among the implemented filters.

We present an efficient multiple description (MD) source coding scheme to achieve robust communication over unreliable channels. In contrast to the popular signal processing based methods, we propose channel coding principles to correlate the descriptions, and then use this correlation for combating channel impairments. We propose a fast, nearly optimal algorithm that aims to maximize the expected quality at the receiver given the channel state and the side channel rates. Our scheme can be used in conjunction with any source coder that is scalable, and is most easily matched to coders outputting a progressive bitstream. It has applications to the transmission of audio, images, as well as delay-constrained video signals, and can also be used to achieve reliable multicast transmission over the existing Internet with the use of simple protocols. Comparisons of our scheme on standard test images to some of the existing state-of-the-art signal processing based MD methods suggest that our simple scheme outperforms them by significant margins.