1. Predictive Grayscale Image Coding Scheme Using VQ and BTC
Author: Yu-Chen Hu
Source:
Fundamenta Informaticae 78 (2007) 239–255
Speaker: Meng-Jing Tsai
Date:

2. Outline Introduction The proposed scheme Conclusion
Block prediction technique
Vector quantization (VQ)
Block truncation coding (BTC)
Conclusion

3. Introduction
A predictive image compression scheme that combines the advantages of vector quantization and moment preserving block truncation coding to provide good image quality at a low bit rate.

4. VQ is widely used because it has a simple decoding structure and requires a low bit rate. However, a lot of computational costs are needed in the codebook design procedure and the image coding procedure.
BTC has the advantages of requiring very low computational cost and providing good
reconstructed image quality. However, a high bitrate is needed in BTC.

5. To reduce the bit rate

6. Vector quantization (VQ)
image vector :
x=(x1,x2,…,xk)
d(x,y1)
d(x,y2) … …
d(x,yN)
Codebook
Original image
index of ymin

7. VQ encoding procedure Find Closest codeword Codebook 1 Image vector 21 2
255
254
253
(12,14,13,16)
(20,25,17,11)
Image vector
(21,44,45,78)
(24,45,47,76)
Index 2
(78,22,77,20)
(33,32,97,98)
(20,55,53,26)

8. VQ encoding procedure w h
Image
Index table
Index table

9. VQ decoding procedure w h
Image
Index table
Index table

10. Block truncation coding (BTC)
區塊臨界值=區塊平均值

11. ≤

12. Block prediction technique

13. ≤

14. ≤ ≤

15. BTC

16. ≤

19. Experimental results of image quality of related BTC scheme and VQ with different codebook sizes

20. Results of block prediction technique for BTC and VQ with different search range sr

23. Conclusion
This scheme exploits the block similarities among neighboring image blocks to improve the bit rate of traditional hybrid methods based on VQ and BTC.
By choosing adequate controlling thresholds, the proposed scheme provides good image quality at a low bit rate.