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

Multiple-Description Lattice Vector Quantization For Image And Video Coding Based On Coincidings Similar Sublattices Of An

Nowadays applications of multimedia communication are found everywhere. Digital communication systems deal with representation of digital data for either storage or transmission. The size of the digital data is a crucial factor for storage and error resiliency of the data is a crucial factor for transmission systems. Thus, it is required to have more efficient encoding algorithms in terms of compression and error resiliency. Multiple-description (MD) coding has been a popular choice for robust data transmission over unreliable network channels

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

Survey of error concealment schemes for real-time audio transmission systems

This thesis presents an overview of the main strategies employed for error detection and error concealment in different real-time transmission systems for digital audio. The “Adaptive Differential Pulse-Code Modulation (ADPCM)”, the “Audio Processing Technology Apt-x100”, the “Extended Adaptive Multi-Rate Wideband (AMR-WB+)”, the “Advanced Audio Coding (AAC)”, the “MPEG-1 Audio Layer II (MP2)”, the “MPEG-1 Audio Layer III (MP3)” and finally the “Adaptive Transform Coder 3 (AC3)” are considered. As an example of error management, a simulation of the AMR-WB+ codec is included. The simulation allows an evaluation of the mechanisms included in the codec definition and enables also an evaluation of the different bit error sensitivities of the encoded audio payload.Ingeniería Técnica en Telemátic

Multiple Description Wavelet-Based Image Coding Using Iterated Function System

Recent literature highlights the multiple description coding (MDC) as a promising method to solve the problem of resilient image coding over error-prone networks, where packet losses occur. In this paper, we introduce a novel multiple description wavelet-based image coding scheme using fractal. This scheme exploits the fractal’s ability, which is to describe the different resolution scale similarity (redundancy) among wavelet coefficient blocks. When one description is lost, the lost information can be reconstructed by the proposed iterated function system (IFS) recovering scheme with the similarity and some introduced information. Compared with the referenced methods, the experimental results suggest that the proposed scheme can achieve better performance. Furthermore, it is substantiated to be more robust for images transmission and better subjective quality in reconstructed images even with high packet loss ratios

Dynamic information and constraints in source and channel coding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 237-251).This thesis explore dynamics in source coding and channel coding. We begin by introducing the idea of distortion side information, which does not directly depend on the source but instead affects the distortion measure. Such distortion side information is not only useful at the encoder but under certain conditions knowing it at the encoder is optimal and knowing it at the decoder is useless. Thus distortion side information is a natural complement to Wyner-Ziv side information and may be useful in exploiting properties of the human perceptual system as well as in sensor or control applications. In addition to developing the theoretical limits of source coding with distortion side information, we also construct practical quantizers based on lattices and codes on graphs. Our use of codes on graphs is also of independent interest since it highlights some issues in translating the success of turbo and LDPC codes into the realm of source coding. Finally, to explore the dynamics of side information correlated with the source, we consider fixed lag side information at the decoder. We focus on the special case of perfect side information with unit lag corresponding to source coding with feedforward (the dual of channel coding with feedback).(cont.) Using duality, we develop a linear complexity algorithm which exploits the feedforward information to achieve the rate-distortion bound. The second part of the thesis focuses on channel dynamics in communication by introducing a new system model to study delay in streaming applications. We first consider an adversarial channel model where at any time the channel may suffer a burst of degraded performance (e.g., due to signal fading, interference, or congestion) and prove a coding theorem for the minimum decoding delay required to recover from such a burst. Our coding theorem illustrates the relationship between the structure of a code, the dynamics of the channel, and the resulting decoding delay. We also consider more general channel dynamics. Specifically, we prove a coding theorem establishing that, for certain collections of channel ensembles, delay-universal codes exist that simultaneously achieve the best delay for any channel in the collection. Practical constructions with low encoding and decoding complexity are described for both cases.(cont.) Finally, we also consider architectures consisting of both source and channel coding which deal with channel dynamics by spreading information over space, frequency, multiple antennas, or alternate transmission paths in a network to avoid coding delays. Specifically, we explore whether the inherent diversity in such parallel channels should be exploited at the application layer via multiple description source coding, at the physical layer via parallel channel coding, or through some combination of joint source-channel coding. For on-off channel models application layer diversity architectures achieve better performance while for channels with a continuous range of reception quality (e.g., additive Gaussian noise channels with Rayleigh fading), the reverse is true. Joint source-channel coding achieves the best of both by performing as well as application layer diversity for on-off channels and as well as physical layer diversity for continuous channels.by Emin Martinian.Ph.D