scholarly journals Central Decoding for Multiple Description Codes based on Domain Partitioning

10.14311/852 ◽  
2006 ◽  
Vol 46 (4) ◽  
Author(s):  
M. Spiertz ◽  
T. Rusert
Keyword(s):  
Packet Loss ◽  
Image Data ◽  
Simple Approach ◽  
Transform Coding ◽  
Multiple Description ◽  
Reconstruction Quality ◽  
Loss Resistance ◽  
Source Data ◽  
Domain Partitioning

Multiple Description Codes (MDC) can be used to trade redundancy against packet loss resistance for transmitting data over lossy diversity networks. In this work we focus on MD transform coding based on domain partitioning. Compared to Vaishampayan’s quantizer based MDC, domain based MD coding is a simple approach for generating different descriptions, by using different quantizers for each description. Commonly, only the highest rate quantizer is used for reconstruction. In this paper we investigate the benefit of using the lower rate quantizers to enhance the reconstruction quality at decoder side. The comparison is done on artificial source data and on image data. 

Multiple description image compression based on multiwavelets

2019 ◽  
Vol 17 (01) ◽  
pp. 1850063
Author(s):  
Ning Wang ◽  
Shuangkui Ge ◽  
Baobin Li ◽  
Lizhong Peng
Keyword(s):  
Image Coding ◽  
Packet Loss ◽  
Source Coding ◽  
Statistical Correlation ◽  
Optimization Criterion ◽  
Multiple Description Coding ◽  
Transform Coding ◽  
Original Image ◽  
Multiple Description ◽  
Description Coding

Multiple description coding (MDC) is one of the source coding techniques to alleviate the problems of packet loss in the network. The decoder estimates the lost signals from received ones, based on the certain statistical correlation between descriptions. However, this correlation also leads to compression redundancy at the same time. Therefore, how to make efficient use of the introduced correlation has great importance in practical MDC approaches. In this paper, we propose a multiple description image coding scenario based on balanced multiwavelets. Two simple and effective methods to reconstruct the original image from partial descriptions are suggested. Furthermore, optimization criterion corresponding to this multiwavelet based system is provided. According to this criterion, we can choose appropriate multifilter banks to satisfy different demands. Experimental results show that the optimized multifilter banks in a simulated transform coding environment perform very well.

Multiple Description Based on Fractal

2015 ◽  
Vol 19 (3) ◽  
pp. 474-478
Author(s):  
Jie Yang ◽  
Keyword(s):  
Image Coding ◽  
Packet Loss ◽  
The Other ◽  
Loss Ratio ◽  
Robust Performance ◽  
Multiple Description ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Reconstruction Quality ◽  
Different Parts

In this paper, I design and develop a multiple description (MD) method which is based on fractal image coding procedure. In the encoder of MD, the IFS generated mappings are separated into different parts and encoded into different descriptions so that, on each description, a subset of these mappings can be involved. Meanwhile, a desired amount of redundancy is inserted into each description such that a satisfactory reconstruction quality will be ensured. In MD decoder, the redundancy and the mappings in one description are exploited to recover the missed mappings in the other description when only one description is received. Compared with the referenced methods, the the proposed MD coder can achieve better and more robust performance under various packet loss ratio circumstance.

scholarly journals P2P video streaming combining SVC and MDC

2011 ◽  
Vol 21 (2) ◽  
pp. 295-306 ◽  
Author(s):  
Francisco de Asís López-Fuentes
Keyword(s):  
Video Coding ◽  
Video Streaming ◽  
Packet Loss ◽  
Scalable Video Coding ◽  
Video Quality ◽  
Scalable Video ◽  
Video Content ◽  
Multiple Description ◽  
Video Distribution ◽  
P2p Video Streaming

P2P video streaming combining SVC and MDC In this paper we propose and evaluate a combined SVC-MDC (Scalable Video Coding & Multiple Description Video Coding) video coding scheme for Peer-to-Peer (P2P) video multicast. The proposed scheme is based on a full cooperation established between the peer sites, which contribute their upload capacity during video distribution. The source site splits the video content into many small blocks and assigns each block to a single peer for redistribution. Our solution is implemented in a fully meshed P2P network in which peers are connected to each other via UDP (User Datagram Protocol) links. The video content is encoded by using the Scalable Video Coding (SVC) method. We present a flow control mechanism that allows us to optimize dynamically the overall throughput and to automatically adjust video quality for each peer. Thus, peers with different upload capacity receive different video quality. We also combine the SVC method with Multiple Description Coding (MDC) to alleviate the packet loss problem. We implemented and tested this approach in the PlanetLab infrastructure. The obtained results show that our solution achieves good performance and remarkable video quality in the presence of packet loss.

scholarly journals Principal Components Analysis of Multispectral Image Data

2004 ◽  
Vol 12 (5) ◽  
pp. 36-39 ◽  
Author(s):  
Brent Neal ◽  
John C. Russ
Keyword(s):  
Principal Components Analysis ◽  
20Th Century ◽  
Principal Components ◽  
Principal Axis ◽  
Image Data ◽  
Data Sets ◽  
Principal Axes ◽  
Source Data ◽  
Components Analysis ◽  
The One

Principal components analysis of multivariate data sets is a standard statistical method that was developed in the early halt or the 20th century. It provides researchers with a method for transforming their source data axes into a set of orthogonal principal axes and ranks. The rank for each axis in the principal set represents the significance of that axis as defined by the variance in the data along that axis. Thus, the first principal axis is the one with the greatest amount of scatter in the data and consequently the greatest amount of contrast and information, while the last principal axis represents the least amount of information.

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