WYDP29

Progressive significance map and its application to Error-resilient image transmission

Published in:Image Processing, IEEE Transactions on (Volume:21 , Issue: 7 )

Page(s):3229 - 3238

ISSN :1057-7149

INSPEC Accession Number:12786452

DOI:10.1109/TIP.2012.2190084

Date of Publication :06 March 2012

Date of Current Version :13 June 2012

Issue Date :July 2012

Sponsored by :IEEE Signal Processing Society

PubMed ID :22410335

Publisher:IEEE

Abstract:

Set partition coding (SPC) has shown tremendous success in image compression. Despite its popularity, the lack of error resilience remains a significant challenge to the transmission of images in error-prone environments. In this paper, we propose a novel data representation called the progressive significance map (prog-sig-map) for error-resilient SPC. It structures the significance map (sig-map) into two parts: a high-level summation sig-map and a low-level complementary sig-map (comp-sig-map). Such a structured representation of the sig-map allows us to improve its error-resilient property at the price of only a slight sacrifice in compression efficiency. For example, we have found that a fixed-length coding of the comp-sig-map in the prog-sig-map renders 64% of the coded bitstream insensitive to bit errors, compared with 40% with that of the conventional sig-map. Simulation results have shown that the prog-sig-map can achieve highly competitive rate–distortion performance for binary symmetric channels while maintaining low computational complexity. Moreover, we note that prog-sig-map is complementary to existing independent packetization and channel-coding-based error-resilient approaches and readily lends itself to other source coding applications such as distributed video coding.

Set Partition Coding (SPC) has been widely studiedin image and video compression. From Shapiro’s pioneeringwork on embedded zerotree wavelet coding tothe JPEG 2000 , image compression standard and thenew 2-D lossy-to-lossless compression algorithm recentlystandardized by the Consultative Committee for Space DataSystems (CCSDS), SPC has served as a key componentto the coding efficiency of wavelet-based approaches. SPC hasalso been successfully applied into several other well-knownimage coding algorithms including SPIHT, SPECK,SWEET , SBHP, and EZBC. As an effective wayof representing image data in the wavelet domain, SPC takes advantage of the characteristicsof wavelet transforms. Additionally, SPC can easily supportdesirable features such as precise rate control and progressivetransmission.

Block Diagram:

Applications:

  1. Television.
  2. Image transmition.

Advantageous:

  1. High PSNR.

Output results:

(a)Input image (b) Output image

  • PSNR performance is 45.43 dB.

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