An analysis of some common scanning techniques for lossless image coding

Nasir Memon, David L. Neuhoff, Sunil Shende

Research output: Contribution to journalArticle

Abstract

Though most image coding techniques use a raster scan to order pixels prior to coding, Hilbert and other scans have been proposed as having better performance due to their superior locality preserving properties. However, a general understanding of the merits of various scans has been lacking. This paper develops an approach for quantitatively analyzing the effect of pixel scan order for context-based, predictive lossless image compression and uses it to compare raster, Hilbert, random and hierarchical scans. Specifically, for a quantized-Gaussian image model and a given scan order, it shows how the encoding rate can be estimated from the frequencies with which various pixel configurations are available as previously scanned contexts, and from the corresponding conditional differential entropies. Formulas are derived for such context frequencies and entropies. Assuming an isotropic image model and contexts consisting of previously scanned adjacent pixels, it is found that the raster scan is better than the Hilbert scan which is often used in compression applications due to its locality preserving properties. The hierarchical scan is better still, though it is based on nonadjacent contexts. The random scan is the worst of the four considered. Extensions and implications of the results to lossy coding are also discussed.

Original languageEnglish (US)
Pages (from-to)1837-1848
Number of pages12
JournalIEEE Transactions on Image Processing
Volume9
Issue number11
DOIs
StatePublished - 2000

Fingerprint

Image Coding
Image coding
Scanning
Pixels
Pixel
Hilbert
Image Model
Entropy
Locality
Coding
Lossless Image Compression
Image compression
Gaussian Model
Encoding
Compression
Adjacent
Context
Configuration

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

Cite this

An analysis of some common scanning techniques for lossless image coding. / Memon, Nasir; Neuhoff, David L.; Shende, Sunil.

In: IEEE Transactions on Image Processing, Vol. 9, No. 11, 2000, p. 1837-1848.

Research output: Contribution to journalArticle

Memon, Nasir ; Neuhoff, David L. ; Shende, Sunil. / An analysis of some common scanning techniques for lossless image coding. In: IEEE Transactions on Image Processing. 2000 ; Vol. 9, No. 11. pp. 1837-1848.
@article{06e99ab1531744e4a56fb8041789ab61,
title = "An analysis of some common scanning techniques for lossless image coding",
abstract = "Though most image coding techniques use a raster scan to order pixels prior to coding, Hilbert and other scans have been proposed as having better performance due to their superior locality preserving properties. However, a general understanding of the merits of various scans has been lacking. This paper develops an approach for quantitatively analyzing the effect of pixel scan order for context-based, predictive lossless image compression and uses it to compare raster, Hilbert, random and hierarchical scans. Specifically, for a quantized-Gaussian image model and a given scan order, it shows how the encoding rate can be estimated from the frequencies with which various pixel configurations are available as previously scanned contexts, and from the corresponding conditional differential entropies. Formulas are derived for such context frequencies and entropies. Assuming an isotropic image model and contexts consisting of previously scanned adjacent pixels, it is found that the raster scan is better than the Hilbert scan which is often used in compression applications due to its locality preserving properties. The hierarchical scan is better still, though it is based on nonadjacent contexts. The random scan is the worst of the four considered. Extensions and implications of the results to lossy coding are also discussed.",
author = "Nasir Memon and Neuhoff, {David L.} and Sunil Shende",
year = "2000",
doi = "10.1109/83.877207",
language = "English (US)",
volume = "9",
pages = "1837--1848",
journal = "IEEE Transactions on Image Processing",
issn = "1057-7149",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",

}

TY - JOUR

T1 - An analysis of some common scanning techniques for lossless image coding

AU - Memon, Nasir

AU - Neuhoff, David L.

AU - Shende, Sunil

PY - 2000

Y1 - 2000

N2 - Though most image coding techniques use a raster scan to order pixels prior to coding, Hilbert and other scans have been proposed as having better performance due to their superior locality preserving properties. However, a general understanding of the merits of various scans has been lacking. This paper develops an approach for quantitatively analyzing the effect of pixel scan order for context-based, predictive lossless image compression and uses it to compare raster, Hilbert, random and hierarchical scans. Specifically, for a quantized-Gaussian image model and a given scan order, it shows how the encoding rate can be estimated from the frequencies with which various pixel configurations are available as previously scanned contexts, and from the corresponding conditional differential entropies. Formulas are derived for such context frequencies and entropies. Assuming an isotropic image model and contexts consisting of previously scanned adjacent pixels, it is found that the raster scan is better than the Hilbert scan which is often used in compression applications due to its locality preserving properties. The hierarchical scan is better still, though it is based on nonadjacent contexts. The random scan is the worst of the four considered. Extensions and implications of the results to lossy coding are also discussed.

AB - Though most image coding techniques use a raster scan to order pixels prior to coding, Hilbert and other scans have been proposed as having better performance due to their superior locality preserving properties. However, a general understanding of the merits of various scans has been lacking. This paper develops an approach for quantitatively analyzing the effect of pixel scan order for context-based, predictive lossless image compression and uses it to compare raster, Hilbert, random and hierarchical scans. Specifically, for a quantized-Gaussian image model and a given scan order, it shows how the encoding rate can be estimated from the frequencies with which various pixel configurations are available as previously scanned contexts, and from the corresponding conditional differential entropies. Formulas are derived for such context frequencies and entropies. Assuming an isotropic image model and contexts consisting of previously scanned adjacent pixels, it is found that the raster scan is better than the Hilbert scan which is often used in compression applications due to its locality preserving properties. The hierarchical scan is better still, though it is based on nonadjacent contexts. The random scan is the worst of the four considered. Extensions and implications of the results to lossy coding are also discussed.

UR - http://www.scopus.com/inward/record.url?scp=0034315911&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034315911&partnerID=8YFLogxK

U2 - 10.1109/83.877207

DO - 10.1109/83.877207

M3 - Article

VL - 9

SP - 1837

EP - 1848

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

SN - 1057-7149

IS - 11

ER -