Author: Russell Everett HenningPublisher:
ISBN:
Category : Astronautics
Languages : en
Pages : 142
Book Description
Evaluation of Channel-optimized Vector Quantization for Coding Images Transmitted by Deep Space Probes
Author: Russell Everett HenningPublisher:
ISBN:
Category : Astronautics
Languages : en
Pages : 142
Book Description
Joint source-channel coding using tree-strctured vector quantization for remote sensing images
Author: Publisher:
ISBN:
Category :
Languages : pt-BR
Pages :
Book Description
Este trabalho estuda o problema de compressão de imagens de sensoriamento remoto segundo a ótica da codificação conjunta fonte-canal. É analisado o desempenho de métodos baseados em quantização vetorial segundo o algoritmo LBG, principalmente o COVQ (Channel Optimized Vector Quantizer) bem como a quantização vetorial estruturada em árvore. Dentro desse contexto, são propostos 2 novos métodos para a resolução do problema: (1)Uma quantização vetorial estruturada em árvores que leva em conta a transmissão através de canais ruidosos, solução denominada COTSVQ (Channel-Design Tree Strutured Vecotr Quantizer), bem como (2) uma classe de métodos que se utiliza de códigos corretores de erro sobre a estrutura progressiva do TSVQ, de forma a proteger os dados de forma ativa durante a transmissão. Os dois métodos propostos podem ser combinados no mesmo compressor, de forma a originar uma classe ampla de compressores adaptados à transmissão por canais com ruído. São apresentados resultados que comparam os desempenhos dos métodos propostos com aqueles já existentes para uma análise de desempenho, na situação de transmissão via satélite de imagens captadas e comprimidas para uma taxa de 1,5bpp. Os resultados mostram que os métodos propostos são muito menos complexos que os já existentes, porém conseguindo atingir uma qualidade de imagem equivalente, ou, em alguns casos, superior.
Channel Optimized Vector Quantization
Author: Hamidreza Ebrahimzadeh SaffarPublisher:
ISBN:
Category :
Languages : en
Pages : 234
Book Description
Joint source-channel coding (JSCC) has emerged to be a major field of research recently. Channel optimized vector quantization (COVQ) is a simple feasible JSCC scheme introduced for communication over practical channels. In this work, we propose an iterative design algorithm, referred to as the iterative maximum a posteriori (MAP) decoded (IMD) algorithm, to improve COVQ systems. Based on this algorithm, we design a COVQ based on symbol MAP hard-decision demodulation that exploits the non-uniformity of the quantization indices probability distribution. The IMD design algorithm consists of a loop which starts by designing a COVQ, obtaining the index source distribution, updating the discrete memoryless channel (DMC) according to the achieved index distribution, and redesigning the COVQ. This loop stops when the point-to-point distortion is minimized. We consider memoryless Gaussian and Gauss-Markov sources transmitted over binary phase-shift keying modulated additive white Gaussian noise (AWGN) and Rayleigh fading channels. Our scheme, which is shown to have less encoding complexity than conventional COVQ and less encoding complexity and storage requirements than soft-decision demodulated (SDD) COVQ systems, is also shown to provide a notable signal-to-distortion ratio (SDR) gain over the conventional COVQ designed for hard-decision demodulated channels while sometimes matching or exceeding the SDD COVQ performance, especially for higher quantization dimensions and/or rates. In addition to our main result, we also propose another iterative algorithm to design SDD COVQ based on the notion of the JSCC error exponent. This system is shown to have some gain over classical SDD COVQ both in terms of the SDR and the exponent itself.
Optimal Redundant Index Assignment for Robust Vector Quantization
Author: Ilju NaA Constrained Joint Source/Channel Coder Design and Vector Quantization of Nonstationary Sources
Author: National Aeronautics and Space Administration (NASA)Publisher: Createspace Independent Publishing Platform
ISBN: 9781721997091
Category :
Languages : en
Pages : 42
Book Description
The emergence of broadband ISDN as the network for the future brings with it the promise of integration of all proposed services in a flexible environment. In order to achieve this flexibility, asynchronous transfer mode (ATM) has been proposed as the transfer technique. During this period a study was conducted on the bridging of network transmission performance and video coding. The successful transmission of variable bit rate video over ATM networks relies on the interaction between the video coding algorithm and the ATM networks. Two aspects of networks that determine the efficiency of video transmission are the resource allocation algorithm and the congestion control algorithm. These are explained in this report. Vector quantization (VQ) is one of the more popular compression techniques to appear in the last twenty years. Numerous compression techniques, which incorporate VQ, have been proposed. While the LBG VQ provides excellent compression, there are also several drawbacks to the use of the LBG quantizers including search complexity and memory requirements, and a mismatch between the codebook and the inputs. The latter mainly stems from the fact that the VQ is generally designed for a specific rate and a specific class of inputs. In this work, an adaptive technique is proposed for vector quantization of images and video sequences. This technique is an extension of the recursively indexed scalar quantization (RISQ) algorithm. Sayood, Khalid and Chen, Y. C. and Nori, S. and Araj, A. Unspecified Center NAG5-1612...
Channel-optimised Source Coding and Vector Quantization with Neural Networks
Author: Lizhong WuRobust Video Coding Using Multiple Description Lattice Vector Quantization with Channel Optimization
Author: Wavelet and Vector Quantization Image Compression for Noisy Channel Transmission
Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 9
Book Description
In the research conducted under this grant, we addressed the problem of transmitting images compressed with high quality wavelet compression algorithms over packet erasure networks, multiple description channels, and noisy communication channels. In addition, we developed new methods for wavelet image compression based on group testing; developed a variation of the set partitioning in hierarchical trees algorithm; and developed a method for fast search of an entropy-constrained vector quantization codebook.
A Study of Vector Quantization for Noisy Channels
Author: Nariman FarvardinPublisher:
ISBN:
Category : Data compression (Telecommunication)
Languages : en
Pages : 35
Book Description
An Optimized Vector Quantization for Color Image Compression
Author: Sastry V. S. KompellaPublisher:
ISBN:
Category : Coding theory
Languages : en
Pages : 110
Book Description