Image Transcoding for Proxy Internet Wireless Access

M. El Shentenawy, A. Gaddah, Q.Guo,

T. Kunz and R. Hafez

Systems and Computer Engineering

Carleton University

http://kunz-pc.sce.carleton.ca/

tkunz@sce.carleton.ca

Overview

Motivation

Related Work

Experimental Setup

Image Classification and Transcoding

Classification: image type (easy), image content (difficult), image properties (maybe)

Transcoding: lossy change of data representation, goal is to reduce data volume while keeping “meaning” as much as possible

Conclusions and Future Work

Transcoding for IP Wireless Access

Application: WWW browsing

Problems:

varying device characteristics

varying user preferences

limited and dynamically changing bandwidth

Solution:

bulk of data traffic are images

catalog images on-line (?)

apply adaptive transcoding techniques

optimization problem: per object/image or whole document

Research Goals and Related Work

Research Goals

identify promising image transcoding operations, based on image classification

predict achievable bandwidth reductions

good adaptive algorithms that meet user constraints, take device limitations and link characteristics into account

adaptive over what timescale, how fast, interactions with link layer

Related Work

image transcoding: GloMop, InfoPyramid, Mowser, Mowgli

all introduce notion of a proxy

range of transcoding operations limited/not dynamically selected

multiple versions provided by server:

WAP: use markup language and protocol stack specific to cellphones, completely new infrastructure

RFC 2295/2296: transparent content negotiation and variant selection, leaves it to server to provide range of formats suited for huge number of clients

Experimental Setup

Step 1: verified that GIF/JPEG images are predominant causes of WWW bandwidth (GIF: 50%, JPEG 32%)

Step 2: collect set of “typical” images using Gozilla, resulting in a collection of 1500 images

Step 3: ensure that images are representative of image size distributions found on WWW

led to additional download of 2000 (smaller) images

Step 4: select image processing software (ImageMagick)

many operations

available in source code

popular and well documented

Initial Results: 4 times the same image?

Initial Results:
Effect of Imaging Software

Classification and Prediction

Resaved all images with ImageMagick to eliminate effect of imaging software

Classification:

applied range of operations, derived compression ratio, tried to correlate it with image properties (size, number of colors, ….)

No success, very limited reason to believe that we could be successful (probability distributions for various parameters)

Most effective operations:

GIF: resize, reduce number of colors, convert to JPEG, convert to grayscale

JPEG: reduce image quality, resize, convert to grayscale, despeckle

Classification and Prediction

More on GIF operations

No stable ranking of most effective operations

Order of operations matters to some extent

Not worthwhile to convert small GIFs to JPEG (less than 1 KByte)

Recommendation: convert to JPEG only when PPB (pixel per bit) is greater than 0.1

More on JPEG operations

Conversion to GIF not advantageous

Ranking stable across most images

Conclusions and Future Research

Defined transcoding algorithms that take user preferences and device characteristics into account, iterate to achieve desired bandwidth reduction

Incorporate algorithms into WWW proxy (Rabbit)

Set up experiments with different client devices (real or emulated): PCs, WinCE PDAs, Palms

Future work:

Evaluate “quality” of transcoded WWW pages (otherwise trivial: drop images completely)

Proxy: cache management (support heterogeneous client population) and performance issues (transcoding is not cheap)

WWW document: how to “transcode” whole document, not just individual images


	M. El Shentenawy, A. Gaddah, Q.Guo,
	T. Kunz and R. Hafez
	Systems and Computer Engineering
	Carleton University
	http://kunz-pc.sce.carleton.ca/
	tkunz@sce.carleton.ca


	Motivation
	Related Work
	Experimental Setup
	Image Classification and Transcoding
		Classification: image type (easy), image content (difficult), image properties (maybe)
		Transcoding: lossy change of data representation, goal is to reduce data volume while keeping “meaning” as much as possible
	Conclusions and Future Work


	Application: WWW browsing
	Problems:
		varying device characteristics
		varying user preferences
		limited and dynamically changing bandwidth
	Solution:
		bulk of data traffic are images
		catalog images on-line (?)
		apply adaptive transcoding techniques
		optimization problem: per object/image or whole document


Research Goals
	identify promising image transcoding operations, based on image classification
	predict achievable bandwidth reductions
	good adaptive algorithms that meet user constraints, take device limitations and link characteristics into account
		adaptive over what timescale, how fast, interactions with link layer
Related Work
	image transcoding: GloMop, InfoPyramid, Mowser, Mowgli
		all introduce notion of a proxy
		range of transcoding operations limited/not dynamically selected
	multiple versions provided by server:
		WAP: use markup language and protocol stack specific to cellphones, completely new infrastructure
		RFC 2295/2296: transparent content negotiation and variant selection, leaves it to server to provide range of formats suited for huge number of clients


	Step 1: verified that GIF/JPEG images are predominant causes of WWW bandwidth (GIF: 50%, JPEG 32%)
	Step 2: collect set of “typical” images using Gozilla, resulting in a collection of 1500 images
	Step 3: ensure that images are representative of image size distributions found on WWW
		led to additional download of 2000 (smaller) images
	Step 4: select image processing software (ImageMagick)
		many operations
		available in source code
		popular and well documented


	Resaved all images with ImageMagick to eliminate effect of imaging software
	Classification:
		applied range of operations, derived compression ratio, tried to correlate it with image properties (size, number of colors, ….)
		No success, very limited reason to believe that we could be successful (probability distributions for various parameters)
	Most effective operations:
		GIF: resize, reduce number of colors, convert to JPEG, convert to grayscale
		JPEG: reduce image quality, resize, convert to grayscale, despeckle


	More on GIF operations
		No stable ranking of most effective operations
		Order of operations matters to some extent
		Not worthwhile to convert small GIFs to JPEG (less than 1 KByte)
		Recommendation: convert to JPEG only when PPB (pixel per bit) is greater than 0.1
	More on JPEG operations
		Conversion to GIF not advantageous
		Ranking stable across most images


	Defined transcoding algorithms that take user preferences and device characteristics into account, iterate to achieve desired bandwidth reduction
	Incorporate algorithms into WWW proxy (Rabbit)
	Set up experiments with different client devices (real or emulated): PCs, WinCE PDAs, Palms
	Future work:
		Evaluate “quality” of transcoded WWW pages (otherwise trivial: drop images completely)
		Proxy: cache management (support heterogeneous client population) and performance issues (transcoding is not cheap)
		WWW document: how to “transcode” whole document, not just individual images