EE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 1 Multimedia Processing EE-5359 Fall-2009 Project Proposal:- Title: Low complexity AVS-M codec by implementing data-mining algorithm Submitted by : - Ramolia Pragnesh Guided by : - Dr. K.R.Rao Dr. Dongil HanEE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 2 Acknowledgement I would like to thank Dr. Rao and Dr. Dongil Han for all their guidance and support. It was the motivation given by them which led to the successful completion of this project. I would also like to thank Dr. Kim for helping me in all the ways he could. And finally I would like to thank all my friends without whose support it would not have possible for me to complete this project.EE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 3 Contents 1. Acronyms 4 2. List of figures 5 3. List of tables 6 4. Background and motivation 7 5. Introduction 8 5.1. Introduction to AVS 8 5.2. Introduction to AVS-M 10 6. Data structure of AVS-M 12 7. Block diagram 16 8. Profile and levels 17 9. Block mode prediction 18 9.1. Intra-prediction 18 9.2. Inter-prediction 21 10. R-D optimization 22 11. Transform, quantization and entropy coding 23 12. Machine learning 25 13. Tools used 29 13.1. Weka tool 29 13.2. Microsoft Visual Studio 2008 29 14. Results 30EE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 4 1. Acronyms: ARFF : Attributes Relation File Format AVS-M : Audio Video Standard for Mobile DIP : Direct Intra Prediction GOP : Group Of Picture ICT : Integer Cosine Transform IMS : Internet Protocol Multimedia Systems IP : Internet Protocol MB : Macro Block MC : Motion Compensation MPEG : Moving Picture Experts Group MV : Motion Vector PBP : Padding before Prediction Weka : Waikato Environment for Knowledge Analysis Q : Quantization QP : Quantization Parameter QCIF : Quarter Common Intermediate Format SCI : Simplified Chrominance Intra-predictionEE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 5 TSFT : Two Steps Four Taps VLC : Variable Length Coding 2. List of figures: Figure 1 : History of A/V coding standard…………………………………………………….8 Figure 2 : Standard Structure of AVS-Video……………………………………………….9 Figure 3: Vertical and horizontal locations of 4:2:0 Y and U, V samples in a frame….13 Figure 4: Slice……………………………………………………………………………………14 Figure 5: Macro-block structure…………………………………………………………………14 Figure 6: Scanning order in a macro-block……………………………………………………..15 Figure 7: Data structre of AVS-M…………………………………………………………….15 Figure 8: Block diagram of AVS-M encoder………………………………………………...16 Figure 9: Block diagram of AVS-M Decoder………………………………………………17 Figure 10: Intra prediction modes…………………………………………………………..19 Figure 11: Intra_chroma_prediction modes in AVS-M……………………………………19 Figure 12: The position of integer, half and quarter pixel samples………………………21 Figure 13: Inter-prediction block sizes………………………………………………………23 Figure 14: Zigzag scanning pattern used for quantized transform coefficients………25 Figure 15: Look-how of the tree generated by weka tool………………………………..28 Figure 16: 45th frame of Akiyo_qcif sequence…………………………………………….31 Figure 17: 50th frame of foreman_cif sequence……………………………………………31EE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 6 Figure 18: 50th frame of foreman_qcif sequence……………………………………….31 Figure 19: Screen shot of .arff file…………………………………………………………32 Figure 20: An example of tree generated by weka tool………………………………….33 3. List of tables: Table 1: different parts of AVS-M………………………………………………………….10 Table 2: most probable mode table…………………………………………………………20 Table 3: Kth order exponential golomb coding…………………………………………….24 Table 4: input parameters defined for encoding sequence……………………………..30 Table 5: AVS-M performance………………………………………………………………..34EE- 5359: Project report Low complexity AVS-M by implementing data-mining algorithm 7 4. Back-ground and motivation: The recent deployment of Internet and mobile networks has greatly contributed to the success and adoption of distributed multimedia communications. Advancement in today‟s digital technology has led to research in digital techniques for encoding and decoding process. Video trafficking is growing not only with respect to internet but also in mobile communication during recent years. Furthermore, HDTV broadcasting applications are widely used due to high resolution and good video quality while the image of standard television transmits with a resolution of 720 x 576 pixels, the high-resolution image has been increased up to 1920 x1080 pixels, which implies a considerable increase in data for transmitting. Since bandwidth and power requirements are important factors to be considered during transmission. Even though it is possible to transmit greater data rates over current communications platforms, it is economically not viable to dedicate such an amount of bandwidth to a single video communication
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