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-Continued 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

Summary of image extraction in DCT domain The source codes in C for image extraction in DCT domain can be downloaded at the Institute’s WEB site: http://futuremedia.szu.edu.cn (Research Space) You are encouraged to run the source codes and test the quality of such extracted images; The image extraction in DCT domain can be further expanded into a number of applications, such as: (i) face recognition; (ii) content-based image retrieval; (iii) fast image/video coding; and (iv) matrix data classification and analysis etc.

Highlight on applications of image extraction in DCT domain (1) (2) For image processing algorithms, you apply (2) to redesign the same algorithms and applications in compressed domain (originality); For pattern recognition problems, you apply (1) to extract PCA features for content analysis, detection, or recognitions but with higher speed and lower complexity (originality);

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 Expanded Research Topic-1: Approximated image and video extraction in compressed domain Input: Compressed codes Derivation of DCT coefficients (partial decoding) Extraction of pixels from (b00, b01, b10, b11) Output: (a00, a01, a10, a11) Image super-resolution to enhance the extracted image quality New Research

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 Expanded Research Topic-2： Face/skin Recognition Input: facial images Facial image decomposition (blocks with variable sizes) LDA feature extraction (b00, b01, b10, b11) Output: Recognized faces Face/skin recognition algorithm design Face/skin recognition algorithm design: Integrated feature description of faces: (i) fusion of LDA features; (ii) cascading LDA features； (iii) projection-based selection of LDA features etc.

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 Expanded Research Topic-3: Content-based image retrieval Input: Compressed codes Content feature extraction in compressed domain Retrieving images with the minimum distances Distance calculation between query and the database

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 Detailed algorithm design （1）Dividing images into blocks with variable sizes, such as 2x2, and approximated LDA feature can be extracted as the four DCT coefficients （2）Integrated content feature construction and distance calculation： （3）Searched image output can be selected corresponding to：

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 Research Alternatives （1）LDA feature extraction: （2）Integrated content description via fusion of LDA features： Weighted integration/fusion： Selected integration/fusion： Projected integration/fusion：

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 Research Exercise: Write a Matlab programme to implement the expanded research topic 2, face detection or recognition inside compressed videos, and complete the relevant experiments. Input: videos Output: Recognized faces Facial image decomposition (blocks with variable sizes) LDA feature extraction (b00, b01, b10, b11) Face/skin recognition algorithm design Key frame extraction Facial region/image detection