Deep Multiple Description Coding by Learning Scalar Quantization

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing datasets for "Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning" can be found in the website of https://github.com/mdcnn/Deep-Multiple-Description-Codin

Three-Description Scalar And Lattice Vector Quantization Techniques For Efficient Data Transmission

In twenty-first century, it has been witness the tremendous growth of communication technology and has had a profound impact on our daily life. Throughout history, advancements in technology and communication have gone hand-in-hand, and the most recent technical developments such as the Internet and mobile devices have achieved in the development of communication to a new phase. Majority of researches who work in Multiple Description Coding (MDC) are interested with only two description coding. However, most of the practical applications necessitate more than two packets of transmission to acquire preferable quality. The goals of this work are to develop three description coding system of scalar quantizers using modified nested index assignment technique at the number of diagonals used in the index assignment of two. Furthermore, this work aims to develop three description lattice vector quantizers using designed labeling function in the four dimensional lattice 4 since it offers more lattice points as neighbours that lead the central decoder to achieve better reconstruction quality. This thesis put emphasis on exploiting three description MDC system using scalar quantizers and lattice vector quantizers. The proposed three description system consists of three encoders and seven decoders (including of one central decoder). A three dimensional modified nested index assignment is implemented in the proposed three description scalar quantization scheme. The index assignment algorithm utilizes a matrix, to indicate the mapping process in the proposed three description scalar quantization scheme. As this thesis suggests a new labeling algorithm that uses lattice 4 for three description MDC system. Projection of a tesseract in four-dimensional space of lattice 4 yields four outputs and the data are transmitted via three channels where one of the outputs is defined as time. The three description quantization system is efficient that provides low distortion and good peak signal-to-noise ratio (PSNR) reconstruction quality. The greater the number of diagonals used in the index assignment, k in MDSQ scheme, the higher quality of the central reconstruction can be accomplished. Simulation results show that the central PSNR is promoted to 34.53 dB at rate of 0.1051 bpp and 38.07 dB at 0.9346 bpp for the proposed three description with 2k= Multiple Description Scalar Quantization (MDSQ) scheme. The percentage gain for the central reconstruction quality is improved from 6.36 % to 18.97 % by the proposed three description scalar quantizer which is at 2k= compared to the renownedMDSQ schemes.Moreover, the proposed three description lattice vector quantization (3DLVQ- 4) scheme outperforms the renowned MDC schemes from 4.4 % to 11.43 %. The central reconstruction quality is promoted to 42.63 dB and the average side reconstruction quality inaugurates 32.13 dB, both at bit rate of 1.0 bpp for the proposed 3DLVQ- 4 scheme

Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

High Quality of Service on Video Streaming in P2P Networks using FST-MDC

Video streaming applications have newly attracted a large number of participants in a distribution network. Traditional client-server based video streaming solutions sustain precious bandwidth provision rate on the server. Recently, several P2P streaming systems have been organized to provide on-demand and live video streaming services on the wireless network at reduced server cost. Peer-to-Peer (P2P) computing is a new pattern to construct disseminated network applications. Typical error control techniques are not very well matched and on the other hand error prone channels has increased greatly for video transmission e.g., over wireless networks and IP. These two facts united together provided the essential motivation for the development of a new set of techniques (error concealment) capable of dealing with transmission errors in video systems. In this paper, we propose an flexible multiple description coding method named as Flexible Spatial-Temporal (FST) which improves error resilience in the sense of frame loss possibilities over independent paths. It introduces combination of both spatial and temporal concealment technique at the receiver and to conceal the lost frames more effectively. Experimental results show that, proposed approach attains reasonable quality of video performance over P2P wireless network.Comment: 11 pages, 8 figures, journa

Efficient compression of motion compensated residuals

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