IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
   
GENERATION OF OPTIMIAL QUANTIZATION TABLE FOR A RATE CONSTRAINED 
VERSION OF JPEG USING GENETIC ALGORITHM 
B.K.Mohan“, A.D.Devasthale^, and E.P.Rao* 
* Centre of Studies in Resources Engineering, IIT, Bombay, Powai, Mumbai-400076, INDIA 
? International Max Planck Research School, Max Planck Inst. of Meteorology, B-20146, Hamburg, Germany 
* Department of Civil Engineering, IIT Bombay, Powai, Mumbai-400076, INDIA 
bkmohan @csre.iitb.ac.in 
KEY WORDS: Remote Sensing, Image Compression, JPEG, Genetic Algorithms 
ABSTRACT: 
Image compression using the JPEG technique has been popular for a long time with imaging device manufacturers, website 
developers, educational content developers and more recently the remote sensing community. The technique has been well 
understood, and several hardware encoders and decoders are available to save in /retrieve from JPEG format. With the increase in 
spatial and radiometric resolution of spaceborne imaging sensors, image compression is a serious consideration and countries like 
India and France are embedding image compression technology in hardware for real time onboard application. The JPEG scheme 
has one limitation in its original definition in that it is not possible to specify a chosen compressed image size by the user. This 
paper describes an attempt at the use of genetic algorithms for generation of JPEG quantization tables using which the desired 
compression ratio can be achieved. 
1. INTRODUCTION 
1.1 Image compression in remote sensing 
Increase in the resolutions of remote sensing imageries has 
resulted in the large volume of data to be handled, processed 
and stored. The on-board or online compression has also 
become very important. Since lossless compression offers very 
small compression ratio, we have to opt for lossy methods. 
Among various lossy compression methods available, the most 
widely used is JPEG method. JPEG method is based on the 
Discrete Cosine Transform followed by the quantization and. 
entropy coding. The extent of compression achieved depends 
upon the coarseness of quantization of the transformed 
coefficients. Since JPEG offers compression based on quality 
factor, which ranges from 0 to100, we cannot specify the exact 
compression ratio or bit-rate while compressing the images. A 
quality factor is the option available for this purpose that 
indirectly leads to generating larger or smaller compressed 
image size. This solution is not often satisfactory since this may . 
result in over-compression causing excessive loss of 
information or under-compression causing overloading of the 
data downlink. Very approaches are published in literature on 
the use of optimisation techniques that can produce close to 
desired size compressed images. JPEG makes use of default 
quantization table for corresponding quality factor. Several 
approaches have been already used to design the quantization 
tables, which will result in specified compression ratio. 
An attempt is made to design an algorithm for optimal 
quantization table to get specified compression ratio for remote 
sensing images using genetic algorithm. Algorithm makes use 
of error occurred in getting desired file size and image 
distortion (expressed in terms of Mean Squared Error) as a 
fitness criteria. 
2. JPEG COMPRESSION 
One of the earliest standards in data compression is the Joint 
Photographic Experts Group (JPEG) defined standard, better 
known as JPEG compression. This involves three main steps: 
a) Discrete Cosine Transform (DCT) of the image 
b) Quantization of the DCT coefficients 
¢) Coding of the quantized coefficients 
Usually the input image is divided into blocks of size 8x8 (Fig. 
1), and each block is separately processed. The degree of 
compression is controlled by a quality factor which determines 
how many of the low magnitude transform coefficients may be 
discarded, or how much compression can be achieved. Greater 
the value of the quality factor, better the quality of 
reconstruction at the cost of reduced compression factor. 
here is no way in the standard formulation for the user to seek 
a desired compression ratio at the best available quality. It is 
known from the JPEG standard that image compression and 
also associated loss occur during quantization of DCT 
coefficients. The quantization step is executed by dividing 
every element of the DCT block by a corresponding element of 
a quantization block or table. A default quantization table is 
specified within the JPEG standard, and a provision is made for 
using custom tables. 
The JPEG committee has released a new standard known as 
JPEG-2000 based on wavelet transform, which is said to have 
overcome all the limitations of JPEG. But since JPEG is 
already being in use from so many years and its real time 
implementation is available, the usage of JPEG will continue 
till the real time implementation of JPEG-2000 standard 
becomes available for remote sensing images.