1. JPEG Compressed Image Retrieval via Statistical Features
Source: Pattern Recognition, Vol. 36, No. 4,
April 2003, pp
Authors: Guocan Feng, Jianmin Jiang
Speaker: Wen-Chuan Wu
Date: 2004/04/19

2. Outline Motivation Proposed scheme Experiments Conclusions
Key construction
Similarity measurement
Experiments
Conclusions

3. Motivation Compressed image retrieval Query JPEG Images
Decompress, Feature extract, similarity
Time consuming,
computationally expensive
JPEG Images

4. Motivation (conts)
DCT domain
low frequency
DCT
IDCT

5. Proposed scheme
Moment extraction
DCT:
IDCT:

6. Proposed scheme (conts)
Example:

7. Proposed scheme (conts)
Compressed image retrieval
DCT coefficients

8. Proposed scheme (conts)
Key construction
0~ ~ ~53 ……. ….…. 93~ ~127 (σ)
0~63
64~127
128~191
192~255
(m) 6 5 4 3 1 2
(m,σ)

9. Proposed scheme (conts)
Similarity Measurement
Database
Query
WDT

10. Experiments Evaluate the performance of a CBIR system:
Retrieval efficiency
database:500 JPEG compressed image
Takes 23 sec. without IDCT
Takes 51 sec. with IDCT
Retrieval effectiveness
Test invariance to the transforms
Test average rank of all relevant images
Test which rank is the first relevant image

11. Experiments (conts)
Retrieval efficiency

12. Experiments (conts)
Retrieval effectiveness - test invariance to the transforms
Database: 100 JPEG compressed images
Query

13. Experiments (conts)
Retrieval effectiveness - average rank of all relevant images
Database: 100 images
AVRR : the average rank of all relevant images.
IAVRR :the ideal average rank. ex: IAVRR=(8-1)/2=3.5
Ratio of AVRR = AVRR / IAVRR. ex: ratio=(39/8)/3.5=1.39
The best: Ratio of AVRR =1

14. Experiments (conts)
Retrieval effectiveness - which rank is first relevant image
Database: 524 JPEG compressed images
Query images: 32

15. Conclusions
Retrieval by directly computing the first and second moments from DCT domain.
Saving computing cost and storage spaces.
Experiments show the retrieval efficiency and effectiveness of the proposed scheme.