HARDEEPSINH JADEJA UTA ID 1000721847 The operation of converting video in one format to another format It is the ability to take existing video content and change the bitrate and or resolution in order to view it on another video playback device MPEG 2 4 has been a widely accepted video coding standard for various applications ranging from DVD to digital tv broadcast The most important goal of MPEG 2 was to make the storage and transmission of digital AV material more efficient However new H 264 AVC standard 1 has an even broader perspective to support high and low bit rate multimedia applications on existing and future networks H 264 requires half of the space to store video content when compared to MPEG 2 1 Hence there is need to find compatibility between MPEG 2 and H 264 devices Fig 1 MPEG 2 encoder decoder 1 Fig 2 H 264 Encoder 9 MPEG 2 H 264 Macroblock prediction with pel accuracy No only pel accuracy yes Macroblock modes 16x16 16x16 16x8 8x16 8x8 8x4 4x8 4x4 Multiple reference prediction no yes Direct modes in B frames no yes Use of B frames as reference frames no Allowed can be selected by the user Fig 3 Different macroblocks in H 264 8 MPEG 2 H 264 Macroblock modes supported 8x8 16x16 with 4 directional modes 4x4 and 8x8 with 9 directional modes Type of intra prediction Fixed prediction of D C coefficient Adaptive directional prediction of 4x4 or 16x6 pixel blocks Transform 8x8 DCT 4x4 Integer transform In H 264 inter frame motion estimation is performed using different MB sizes from 16 16 to 4 4 8 For each MB all different sizes are evaluated and the one leading to the minimum rate distortion RD cost is then selected This guarantees the optimal block size for the final encoding but at the expense of a high computational cost Intra mode selection algorithm 10 Only intra spatial prediction scheme 11 Fast mode decision algorithm for intra prediction for H 264 AVC 12 Dynamic control of motion estimation search parameters for low complexity H 264 13 Adaptive algorithm for fast motion estimation 17 Adaptive algorithm for fast motion estimation in H 264 MPEG 4 AVC 16 12 It is a branch of science which develops algorithms to allow computers to evolve or become smart Machine learning algorithms are applied in large number fields machine vision medical diagnostics image processing wireless communication are just few among them 13 It was developed by J R Quinlan It uses divide and conquer approach to develop a tree Uses two possible criteria to carry out a test at each node of the tree information gain and gain ratio 14 Select number of frames of a video sequence in QCIF and CIF as training sequences Obtain the required attributes off line Encode the training sequence using full complexity H 264 encoder Store the attributes calculated off line and mode decision taken by encoder in ARFF file Feed this ARFF file to weka tool which will give decision tree 15 Mask the motion estimation part in the actual H 264 encoder Overwrite that with if else statements based on the decision tree Compare the performance of the simple cascaded transcoder with our transcoder 16 Creating the Training Files Fig 4 Training algorithm for decision tree 2 Decision Trees Fig 5 Decision tree for deciding the macro block mode of H 264 encoder 1 Results Test sequence Akiyo CIF No of Frames 100 Width 352 Height 288 QP PSNR JM Proposed Bit rate JM Proposed Encoding time JM Proposed 28 40 246 40 194 167 25 170 23 1309 124 364 59 32 37 654 37 602 103 24 105 07 1120 317 312 36 35 186 35 134 68 07 67 246 934 22 260 18 40 32 638 32 586 46 45 47 27 819 643 228 27 PSNR 0 052 dB Bitrate kbps 1 78 Encoding time sec 72 55 Table 1 Time PSNR and Bit rate for Akiyo CIF sequence Test sequence Akiyo qcif No of Frames 100 Width 176 Height 144 QP PSNR JM Proposed Bit rate JM Proposed Encoding time JM Proposed 28 38 775 38 738 60 52 61 076 220 425 62 049 32 35 783 35 746 39 87 40 236 216 823 61 0356 36 33 125 33 088 27 08 27 329 211 004 59 397 40 30 515 30 4781 18 98 19 15 183 513 51 659 PSNR 0 037 dB Bitrate kbps 0 92 Encoding time sec 71 85 Table 2 Time PSNR and Bit rate for Akiyo QCIF sequence Test sequence Paris CIF No of Frames 100 Width 352 Height 288 QP PSNR JM Proposed Bit rate JM Encoding time Proposed JM Proposed 28 36 568 36 489 796 99 825 76 1040 353 280 48 32 33 282 33 203 481 00 498 37 834 965 225 106 36 30 226 30 147 283 88 294 13 799 215 41 40 27 388 27 309 173 26 179 52 797 214 87 PSNR 0 079 dB Bitrate kbps 3 61 Encoding time sec 73 04 Table 3 Time PSNR and Bit rate for ParisCIF sequence Test sequence Paris QCIF No of Frames 100 Width 176 Height 144 QP PSNR JM Proposed Bit rate JM Proposed Encoding time JM Proposed 28 35 757 35 679 618 87 652 165 260 857 72 23 32 32 116 32 046 407 52 429 44 201 505 55 795 36 28 785 28 722 240 44 253 37 199 260 55 175 40 25 707 25 65 140 49 148 05 197 326 54 64 PSNR 0 216 dB Bitrate kbps 5 38 Encoding time sec 72 31 Table 4 Time PSNR and Bit rate for Paris QCIF sequence Test sequence Mobile CIF No of Frames 100 Width 352 Height 288 QP PSNR JM Proposed Bit rate JM Proposed Encoding time JM Proposed 28 35 351 35 339 2317 97 2340 182 1078 516 328 516 32 31 415 31 4031 1215 71 1207 38 1008 546 307 203 36 28 088 28 0763 571 88 571 83 1006 631 306 619 40 25 324 25 312 301 13 304 02 999 369 304 408 PSNR 0 0115 dB Bitrate kbps 0 96 Encoding time sec 69 54 Table 5 Time PSNR and Bit rate for Mobile CIF sequence Test sequence Mobile qcif No of Frames 100 Width 176 Height 144 QP PSNR JM Bit rate Proposed JM Encoding time Proposed JM Proposed 28 34 3 34 186 557 06 579 47 241 382 69 71 32 30 328 30 176 283 61 295 02 233 67 29 36 27 106 26 992 141 146 67 227 65 56 40 24 447 24 333 79 29 82 48 214 875 62 05 PSNR 0 052 dB Bitrate kbps 1 78 Encoding time sec 72 55 Table 6 Time PSNR and Bit rate for Mobile QCIF sequence Bit Rate v s PSNR using JM reference software for CIF 45 40 35 PSNR dB 30 25 akiyo cif 20 paris cif mobile cif 15 10 5 0 0 500 …
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