By:-Ramolia Pragnesh R. Guided by :-Dr. K.R.Rao. Term:-Spring

Motivation:  Increase in demand of multimedia contents over internet and wireless networks.  Bandwidth is the too expensive resource to increase it in proportion to the increase in demand of data.  Video codec plays an important role here, compressing the data with high efficiency tools.  Complexity comes along with high efficiency in codecs.  Implementing hardware solutions for low end devices like mobile, is very expensive and also creates problem of over heating and power consumption. 2

Brief overview of the thesis: 3 Figure 1: Proposed encoder with C4.5

Table of contents:  Overview of AVS-M.  Complexity calculation in AVS-M.  Various approaches to reduce complexity.  Introduction to machine learning and algorithm C4.5  Proposed encoder.  Results.  Future work.  References. 4

Introduction to AVS-M[24] AVS-M is the seventh part of video coding standard developed by AVS working group of China targeting mobile applications. It has 9 different levels for different formats [16]. It supports only progressive video coding hence codes frames only[22]. It uses only 4:2:0 chroma sub-sampling format[22]. It uses only I and P frames[22]. 5

Different parts of AVS [10] PartName 1System 2Video 3Audio 4Conformance test 5Reference software 6Digital media rights management 7Mobile video 8Transmit AVS via IP network 9AVS file format 10Mobile speech and audio coding Table 1: Different parts of AVS 6

Key tools of AVS-M[31]: Network abstraction layer (NAL). Supplemental enhancement information (SEI). Transform –4x4 integer transform. Adaptive quantization of step size varying from Intra prediction –9 modes (Fig. 5 ), simple 4x4 intra prediction and direct intra prediction[25]. Inter prediction –16x16, 16x8, 8x16, 8x8, 8x4, 4x8, and 4x4 block sizes for ME/MC(Fig. 7). Quarter pixel accuracy in motion estimation. Simplified in-loop de-blocking filter. Entropy coding. Error resilience. 7

Layered Data Structure Sequence Picture Slice Macro Block Block Sequence G.O.P. Picture Slice Macro blockBlock 8 Figure 2: Layered data structure of AVS-M

AVS-M Codec[10] Each MB needs to be intra or inter predicted. Switch S0(Fig. 3 ) is used to decide between inter and intra based type of MB. Unit size for intra prediction is block size of 4x4, and predictions are derived based on left and upper blocks. Inter predictions are derived from blocks of varying sizes: 16x16, 16x8, 8x16, 8x8, 8x4, 4x8, and 4x4 from locally reconstructed frames. Transform coefficients are coded by VLC. Deblocking filter is applied on reconstructed image. 9

Encoder Figure 3: Encoder of AVS-M [10] 10

Decoder Figure 4: Decoder of AVS-M [10] 11

Intra adaptive directional prediction [25] Figure 5: Intra adaptive directional prediction 12

Intra prediction Intra prediction scheme in AVS-M brings much simplicity as compared to H.264 baseline profile of H.264. It uses 4x4 block as the unit for intra-prediction. It uses 2 modes of prediction in intra prediction: intra_4x4 and direct intra prediction. Intra_4x4 uses content based most probable intra mode decision as shown in Table 2 to save bits, where U and L represents the upper ad left blocks as shown in Fig. 6. Direct intra prediction brings much of the compression based on trade-off decision. Upper block[U] Left block[L] Current block Fig. 6 : Current block and neighboring block representation [16] 13

Intra prediction U L Table 2: Content based most probable mode decision table [25] Mode ‘-1’ is assigned to ‘L’ or “U’ when the current block does not have ‘Left’ or ‘Upper’ block respectively. 14

Inter-frame prediction Size of the blocks in inter-frame prediction can be 16x16, 16x8, 8x16, 8x8, 8x4, 4x8, and 4x4 depending on the amount of information present within the macro- block[9]. Motion is predicted up to ¼ pixel accuracy. If the half_pixel_mv_flag is 1 then it is up to ½ pixel accuracy. Half pixel and quarter pixel accurate motion vectors are calculated by interpolating the reference frame, by applying filters. (Fig. 8) 15

Inter frame block sizes [9]: 7 block sizes are present in AVS-M for inter frame prediction. Figure 7: Inter frame prediction block sizes 16

sub-pixel motion estimation by interpolation[15][16]: Figure 8: interpolation of sub-pixels (hatched lines show half-pixels, empty circles are quarter-pixels, and capital letters represent full-pixels.) 17

Complexity calculation for AVS-M Variable 7 block sizes in Inter Mode. It supports 9 intra_4*4 mode and 1 Direct_intra prediction mode. Full search for motion estimation gives the optimum result, but that comes along with implementation complexity. For example, assuming FS(full search) and M block types, N reference frames and a search range for each reference frame and block type equal to +/- W, check for N x M x (2W + 1)^2 positions, to find out inter prediction mode and its motion vector, that too inter pixel accurate. 18

Continued… 7 inter prediction modes because of 7 different block sizes, 9 intra_4*4 modes and 1 direct intra prediction mode. ¼ and ½ pixel accuracy in motion vector estimation. 19

Various techniques to reduce complexity Intra mode selection algorithm[26]. Only intra spatial-prediction scheme[27]. Fast mode decision algorithm for intra prediction for H.264/AVC [28]. Dynamic control of motion estimation search parameters for low complexity H.264[29]. Adaptive algorithm for fast motion estimation [30]. Adaptive algorithm for fast motion estimation in H.264/MPEG-4 AVC [4] 20

Introduction to machine learning [32]:  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, fraud transaction detection, image processing, wireless communication and market analysis are just few among them. 21

Machine learning algorithm C4.5[33] It was developed by J. R. Quinlan. It is descendant of ID3 and CLS [c4.5 doc]. 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. Initial tree is pruned to avoid overfitting, which introduces errors in prediction. 22

Proposed encoder: 23

Implementation steps [2]:- Select number of frames of a video sequence in QCIF as training sequences. Obtain the required attributes off line Encode the training sequence using full complexity AVS-M 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 similar to that of figure

Continued… Mask the motion estimation part in the actual AVS-M encoder Overwrite that with if-else statements based on the decision tree Compare the performance of the simple codec with actual AVS-M 25

Decision tree used in encoder: 26 Figure : C4.5 decision tree for mode decision.

27

Comparison of the encoding time: Sequence no:Sequence Encoding time of the sequence with original AVS-M (seconds) Encoding time of the sequence with machine learning algorithm implemented (seconds) % reduction in encoding time of the sequence ** 1.Akiyo_qcif Highway_qcif Coastguard_qcif Bridge-close News_qcif Miss-america_qcif Container_qcif Carphone_qcif Foreman_qcif

Bar chart comparing encoding time (sec.): 29

Comparison of the PSNR (Y- dB) Sequence numberSequence PSNR of the video sequence encoded with AVS-M (Y-component in dB) PSNR of the video sequence encoded with AVS-M, with machine learning algorithm (Y- component in dB) % *** reduction in PSNR of Y-component while encoding with proposed encoder 1.Akiyo_qcif Highway_qcif Coastguard_qcif Bridge-close News_qcif Miss-america_qcif Container_qcif Carphone_qcif Foreman_qcif

Bar chart comparing PSNR (Y in dB) 31

Comparison of the PSNR (U in dB) Sequence numberSequence PSNR of the video sequence encoded with AVS-M (U-component in dB) PSNR of the video sequence encoded with AVS-M, with machine learning algorithm (U- component in dB) %**** reduction in PSNR of U-component while encoding with proposed encoder 1.Akiyo_qcif Highway_qcif Coastguard_qcif Bridge-close News_qcif Miss-america_qcif Container_qcif Carphone_qcif Foreman_qcif

Bar chart comparing PSNR (U in dB) 33

Comparison of the PSNR (V in dB) Sequence numberSequence PSNR of the video sequence encoded with AVS-M (V-component in dB) PSNR of the video sequence encoded with AVS-M, with machine learning algorithm (V- component in dB) %***** reduction in PSNR of V-component while encoding with proposed encoder 1.Akiyo_qcif Highway_qcif Coastguard_qcif Bridge-close News_qcif Miss-america_qcif Container_qcif Carphone_qcif Foreman_qcif

Bar chart comparing PSNR (V in dB) 35

Comparison of PSNR (YUV in dB) Sequence numberSequence PSNR of the video sequence encoded with AVS-M (YUV-component in dB) PSNR of the video sequence encoded with AVS-M, with machine learning algorithm (YUV- component in dB) % reduction in PSNR of YUV-component while encoding with proposed encoder 1.Akiyo_qcif Highway_qcif Coastguard_qcif Bridge-close News_qcif Miss-america_qcif Container_qcif Carphone_qcif Foreman_qcif

Bar chart comparing PSNR(YUV in dB) 37

Comparison of number of bits used Sequence numberSequence No. of bits used to encode the sequence encoding with AVS-M No. of bits used to encode the sequence encoding with the proposed encoder. %****** increase in bits while encoding with proposed encoder 1.Akiyo_qcif Highway_qcif Coastguard_qcif Bridge-close News_qcif Miss-america_qcif Container_qcif Carphone_qcif Foreman_qcif

Bar chart comparing # of bits: 39

Comparison of performances of AVS-M and proposed encoder SequenceAkiyo_qcifContainer_qcifBridge-close_qcifMiss-america_qcifForeman_qcif PSNR(Y) dB PSNR(Y’) dB %decrease in PSNR PSNR(U) dB PSNR(U’) dB %decrease in PSNR PSNR(V) dB PSNRV’) dB %decrease in PSNR

Continued… SequenceAkiyo_qcifContainer_qcifBridge-close_qcifMiss-america_qcifForeman_qcif PSNR(YUV) dB PSNR(YUV’) dB %decrease in PSNR No. of bits used(B) No. of bits used(B’) % saving in bits Encoding time(t) (t) Encoding time (t’) % decrease in encoding time

Conclusions: Implementing C4.5 in AVS-M, reduces the encoding time of a sequence by 75%-80%. Except for Foreman_qcif and Carphone_qcif, there is no considerable loss in PSNR (Y) component. There is almost no loss in PSNR of U and V components. Surprisingly, there are considerable savings in number of bits used to encode the sequence, which is a bonus. The tree trained for 4 frames works also for 100 frames and for all sequences. 42

Future work: Intense study in pattern recognition and machine learning can be undertaken to develop a better tree. Deep study of video attributes and their final impact in mode decision can also help us in developing a good decision tree. Other machine learning algorithms can be implemented in AVS-M or other codecs to reduce the complexity. The bitstream obtained by this encoder can be multiplexed with some audio bitstream for streaming applications. 43

References: [1] project%20report%20final.pdf ; course website UTAhttp://ee.uta.edu/Dip/Courses/EE5359/Multimedia%20Processing%20 project%20report%20final.pdf [2]P. Carrillo, H. Kalva and T. Pin “Low complexity H.264 video encoding", SPIE. vol.7443, Paper # 74430A, Aug [3]Kusrini1, S. Hartati, ”Implementation of C4.5 algorithm to evaluate the cancellation possibility of new student applicants at STMIK AMIKOM YOGYKARTA”, Proceedings of the International Conference on Electrical Engineering and Informatics Institute Teknologi Bandung, Indonesia June 17-19, 2007 [4]S. Saponara, et al “Adaptive algorithm for fast motion estimation in H.264/MPEG-4 AVC”, Proc. Eusipco2004, pp. 569 – 572, Wien, Sept [5]Decision tree basics : [6]Weka Tools software : 44

Continued… [7]X. Jing and L. P. Chua, “An efficient inter mode decision approach for H.264 video coding” International Conference on Multimedia and Expo (ICME), pp , July [8]Software download: ftp:// /public/avs_doc/avs_software ftp:// /public/avs_doc/avs_software [9] Power point slides by L.Yu, chair of AVS video : ee.uta.edu/dip/Courses/EE5351/ISPACSAVS.pdfhttp://www- ee.uta.edu/dip/Courses/EE5351/ISPACSAVS.pdf [10]L. Fan, “Mobile multimedia broadcasting standards”, ISBN: , Springer US, 2009 [11]AVS working group official website, [12]Test sequences can be downloaded from the site [13]Y. Xiang et al., “Perceptual evaluation of AVS-M based on mobile platform”, Congress on Image and Signal Processing, 2008, vol. 2, Issue, pp.76 – 79, May

Continued… [14]M.Liu and Z.Wei. “A fast mode decision algorithm for intra prediction in AVS-M video coding”, vol.1, ICWAPR apos; 07, Issue, 2-4, pp.326 – 331, Nov [15]L.Yu et al, “Overview of AVS-Video: Tools, performance and complexity,” SPIE VCIP, vol. 5960, pp ~ , Beijing, China, July [16]L.Yu, S.Chen, J.Wang, “Overview of AVS-video coding standards” special issue on AVS, SP:IC, vol. 24, p , April [17]Y. Shen, et al, “A simplified intra prediction method”, AVS Doc. AVS-M 1419, [18]F.Yi, et al, “An improvement of intra prediction mode coding”, AVS Doc. AVS-M 1456, [19]L. Xiong, “Improvement of chroma intra prediction”, AVS Doc. AVS- M1379,

Continued… [20]X.Mao, et al, “Adaptive block size coding for AVS-X profile”. AVS Doc. AVS-M2372, [21]R.Wang, et al, “Sub-pixel motion compensation interpolation filter in AVS”, 2004 IEEE International Conference on Multimedia and Expo, 1:93-96, [22]F.Yi et al, “Low-complexity tools in AVS Part 7”, J. Computer Science Technology, vol.21, pp , May [23]W.Gao and T.Huang, “AVS Standard -Status and Future Plan”, Workshop on Multimedia New Technologies and Application, Shenzhen, China, Oct [24]W.Gao et al, “AVS– the Chinese next-generation video coding standard,” National Association of Broadcasters, Las Vegas, [25] Z.Ma, et al, “Intra coding of AVS Part 7 video coding standard”, J. Computer Science Technology, vol.21, Feb

Continued… [26] J. Kim et al, “H.264 Intra Mode Decision for Reducing Complexity Using Directional Masks and Neighboring Modes”, PSIVT 2006, LNCS 4319, pp. 959 – 968, [27]Xin, Vetro, “Fast Mode Decision for Intra-only H.264/AVC Coding”, TR May [28]Pan et. al “Fast Mode Decision Algorithm for Intra prediction in H.264/AVC Video Coding”, IEEE Transactions On Circuits And Systems For Video Technology. Vol 15, No. 7, July 2005 [29]S. Saponara et. al “Dynamic Control of Motion Estimation Search Parameters for Low Complex H.264 Video Coding”, IEEE Transactions on Consumer Electronics, Vol. 52, No. 1, FEBRUARY

Continued… [30]Cheng-Chang Lien, Chung-Ping Yu, “A Fast Mode Decision Method for H.264/AVC Using the Spatial-Temporal Prediction Scheme”, ICPR 2006 [31] Information technology- advanced coding of audio and video part -7: mobile video [32] O. Maimon, L. Rokach, “ The data mining and knowledge discovery handbook”, Springer publication [33] X. Wu et al, “Top 10 algorithms in data mining” survey paper, Springer-Verlag London Limited