Research Institute for Future Media Computing 未来媒体技术与计算研究所 Research Institute for Future Media Computing http://futuremedia.szu.edu.cn 7. DCT-based Research Topics 江健民，国家千人计划特聘教授 深圳大学未来媒体技术与计算研究所所长 Office Room: 409 Email: jianmin.jiang@szu.edu.cn http://futuremedia.szu.edu.cn

Research Road Map Input Information or Data Visualization to convert the data into 2D or other multi-dimensional sets Data transform via Fourier, DCT, etc. Feature extraction and analysis in transform domain

7.2 Extraction of Block Edge Patterns H. S. Chang and K. Kang, “A compressed-domain scheme for classifying block edge patterns,” IEEE Trans. Image Process., vol. 14, no. 2, pp. 145–151, Feb. 2005. Jiang J., Qiu K. and G. Xiao (2008) “An edge block content descriptor for MPEG compressed videos”, IEEE Transactions on Circuits, Systems and Video Tech. Vol 18, No 7, pp 994-998;

Edge Orientation Analysis in Pixel Domain Horizontal edge measurement:

Vertical edge measurement: Diagonal edge (45 degrees) measurement:

Diagonal edge (135 degrees) measurement: No-edge measurement: An emphasis factor

Existing Work [1]: Edge Orientation Analysis in DCT Domain Edge orientation measurement in compressed domain:

Block-Edge-Pattern (BEP) Detection:

Exploiting image extraction technique to redesign the BEP detection algorithm We have:

Exploiting image extraction technique to redesign the BEP detection algorithm Existing work:

Existing work fix the no-edge value With our idea, the no-edge measurement can also be worked out in compressed domain: New EBP detection:

Summary This is a new idea for research, and thus you are encouraged to implement the BEP detection algorithm and come to see me for further discussion if you wish; You can start by downloading the source codes written in C from the Institute’s WEB site, and run the programme to extract the DCT coefficients; In comparison with the existing work [1], the new BEP detection has the advantage that no fixed threshold value is needed for detecting no-edge orientations, and hence adaptive to the input image; Further research can be done to extend to video BEP detection, and use the BEP feature for other applications, such as pattern recognitions etc.

An example of the experiment design JPEG compressed images Extract the first four DCT coefficients (b00, b01, b10, b11) Producing edge-label images Compare with the existing algorithm [1] Results and analysis Fist four DCT coefficients extraction from videos BEP detection from videos Results and analysis