1. Image Compression Using Address-Vector Quantization NASSER M. NASRABADI, and YUSHU FENG Presented by 蔡進義 P9218219 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 38, NO. 12, DECEMBER 1990

2. 2 Outline  Introduction  Address-Vector Quantization  Address-Codebook and Block-Transition Probability Matrix  Design of the LBG-Codebook  Experimental Results  Conclusion

3. 3 Introduction  Vector quantization techniques have been used for a number of years for coding of digital image.  LBG algorithm  The LBG algorithm is very much dependent on the content of the codevectors in the initial codebook and it is local minimum.  Siumlated Annealing (SA)  Address-Vector Quantization  Dynamic A-VQ  Multilayered A-VQ

4. 4 Address-Vector Quantization  Exploit the interblock correlation of the statistical redundancy between the blocks in order to reduce the bit rate.  Address-Vector Quantization  Each codevector represents a combination of address.  Each element of this codevector is an address of an entry in the LBG-codebook. A-VQ LBG-codebook image

5. 5 Address-Vector Quantization  The A-VQ coding system consists of two major components:  A codebook made up of two parts  LBG-codebook  Address-codebook  Four block-transition probability (frequency) matrices each giving the frequency occurrence of two neighboring blocks in  Vertical  Horizontal  45 0 -diagonal  135 0 -diagonal

6. 6 Address-Vector Quantization  The address-codebook is assumed to include all the possible address combination that are encountered during the training process.  The structural information in the image is exploited by the address-codebook to encode four neighboring blocks together as unit.  Only the active region of the address-codebook is addressable by the encoder and decoder.  The most possible address combination

7. 7 Address-Vector Quantization  Each block-transition probability matrix contains the conditional probability of a codevector occurring given one of its neighboring horizontal, vertical or any of the two diagonal codevectors.

8. 8 Address-Codebook and Block-Transition Probability Matrix

9. 9 Address-Codebook Design  The address-codebook is obtained by dividing all the images in the training sequence into small blocks.  Extract all the possible address combination of four neighboring blocks occurring together in the training sequence.  If the LBG-codebook size is N=128, and the dimension of the codevector in the address-codebook is d=4, then the total possible combination is N d =128 4.

10. 10 Design of the LBG-Codebook  To extend the (mean/residual vector quantizer) M/RVQ coding system to a predicted mean/residual vector quantizer

11. 11 Encoding-Decoding Process  The transmitter and receiver have  The same codebook  The same block-transition probability matrices  A score function

12. 12 Encoding  The four neighboring blocks are coded either by the address codebook or by LBG-codebook.  The four neighboring blocks 1, 2, 3, and 4 are first coded by the LBG-codebook to find corresponding address-codevector.  Score parameter P(1/A) x P(2/A) x P(1/B) x P(2/B) x P(1/C) x P(1/D) x P(3/D) x P(1/E) x P(3/E) X P(2/F) x P(4/1) x P(4/2) x P(4/3)

13. 13 Decoding  A simple lookup table consisting of an LBG-codebook and an address-codebook exactly the same as the encoder.  The address-codebook at the transmitter and the receiver have to be in synchronization.

14. 14 Experimental Results Standard VQ Bit rate: 0.437 bits/pixel

15. 15 Conclusion  A new coding technique, address-vector quantization where interblock correlation is exploited.  A score function is used to calculate a parameter to reorder the contents of the address-codebook to bring the most probably address-codevectors into the region of the codebook.  Disadvantages  Synchronization problem  Computational complexity of reordering the contents of the address-codebook during encoding