This document presents an effective image retrieval system using dot-diffused block truncation coding (DDBTC) features. It discusses two main methods of image retrieval: text-based and content-based. Content-based retrieval analyzes images based on color, texture, or form but requires long processing times. The document then describes DDBTC, which divides color images into blocks, computes quantizers for each block, and generates a bitmap image. This approach reduces processing time for image encoding and retrieval compared to other methods. Experimental results showed the DDBTC features provided an effective image retrieval system for large image databases.

1. Effective Image Retrieval System Using Dot-
Diffused
Block Truncation Coding Features

2. Introduction
 Computer system for browsing, searching and
retrieving images from a large database of digital
images
 Two methods
 Text Based Image Retrieval
 Content Based Image Retrieval
 TBIR
 Requires text as input to search for images
 Uses keywords such as image name, date etc

3. Contd..
 CBIR
 Input as query image
 Search similar images by colour, texture or form as
a comparison
 Requires long processing time due to amount of
images to be analyzed in database

4. Motivation
 Due to the increase of online users on the Internet,
the amount of collections of digital images have
grown continuously during this period. So, it is
necessary to develop an appropriate system to
manage effectively these collections.

5. Block Truncation Coding
 Technique for image compression
 Divide image into multiple non-overlapped image
block
 Each block represented by two quantizers
 High & low mean value
 Bitmap image
 Perform thresholding operation using mean value
to generate bitmap image

6. BTC Image Coding Using Vector
Quantization
 Source encoding the output of a BTC
 Overhead statistical information
 Truncated block
 If vector quantization used on either one of
output- bit rate reduces upto 1.5 bits/pel
 Vector quantization used on both output- bit rate
reduces upto 1.0 bits/pel

7. Contd..
Advantage
 Achieves compression ratio of 8:1
Disadvantage
 Did not remove inter block redundancy

8. BTC-VQ-DCT Hybrid Coding of
Digital Images
 Simple computation & edge preservation of BTC
 High fidelity & high compression ratio of adaptive
DCT
 High compression ratio & good subjective
performance of VQ
Advantage
 Remove both inter block and intra block redundancy
 Computational complexity is less than DCT or VQ
 Compression ratio of 10:1

9. Absolute Moment Block Truncation
Coding
 Digitized image divided into block
 Quantized each block results in same sample mean &
same sample first absolute central moment
 Mean-information about central tendancy
 First absolute central moment- information about
dispersion from mean
Advantage
 Processing time is reduced at transmitter and receiver-
fast BCT

10. Dot-diffused Block Truncation
Coding For Color Image

11. Contd..
 Color image of size MxN in RGB divided into
several non-overlapping image blocks
 Each image block processed independently
 DDBTC encoder generates two extreme
quantizers
 Minimum quantizers
•Maximum quantizers

12. Contd..
 DDBTC encoder
convert colour image
into interband
average
•Mean value is computed
by

13. Contd..
 Diffused weight
•Bitmap image obtained by
•Decoder receives set of min and max quantizer and
bitmap
•Replace 0 with minimum quantizer value and 1 with
maximum quantizer

14. References
[1] J. Guo, H. Prasetyo, and N. Wang, “Effective Image
Retrieval System Using Dot-Diffused Block Truncation
Coding Features”, IEEE Trans. multimedia, vol. 17, no. 9,
september 2015
[2] V. R. Udpikar and J. P. Raina, “BTC image coding using
vector quantization,” IEEE Trans Commun., vol. COM-35,
no. 3, pp. 352–256, Sep.1987.
[3] Y. Wu and D. Coll, “BTC-VQ-DCT hybrid coding of digital
images,” IEEE Trans. Commun., vol. 39, no. 9, pp. 1283–
1287, Sep. 1991.
[4] M. D. Lema and O. R. Mitchell, “Absolute moment block
truncation coding and its application to color images,” IEEE
Trans. Commun., vol. COM-32, no. 10, pp. 1148–1157,
Oct. 1984.