A Novel Fractal Monocular and Stereo Video Codec based on MCP and DCP

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1 A ovel Fractal Monocular and Stereo Video Codec based on MCP and DCP Shiping Zhu a, Zaikuo Wang a, Kamel Belloulata b a Department of Measurement Control and Information Technology, School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 1191, China. b Département d Electronique, Faculté des Sciences de l'ingénieur, Université Djilali Liabès de Si Bel Abbès, Si Bel Abbès,, Algérie. Shiping.Zhu@buaa.edu.cn; wangzaikuo@163.com; Kamel.Belloulata@USherbrooke.ca Abstract-In the paper, a novel fractal monocular video codec is proposed which includes: using new macroblock partition scheme instead of classical quadtree partition scheme; reducing the block searching strategy and range, thus increasing the calculation speed greatly; using homo-i-frame like in H.64; reducing repeating calculation. The fractal monocular video codec uses the MCP structure. And the fractal stereo video codec is proposed which matches the macroblock with two reference frames in left and right views results in increasing compression ratio and reducing bit rate and bandwidth. The proposed fractal stereo video codec combines the MCP and DCP. Experimental results incate the proposed fractal monocular video codec could improve the image quality 13% averagely, raise compression ratio 3-5 times, and reduce compression time 9%; For fractal monocular video codec, the proposed fractal stereo video codec can raise compression ratio 3-5 and improve the image quality.5 db. Index Terms fractal, monocular, stereo, video cong, H.64. I. ITRODUCTIO Fractal cong is based on the Iterative Function System (IFS) [1]. It has been well known that a fractal image codec performs better, in terms of very fast decong process as well as the promise of potentially good compression ratio. But at present, fractal codec is not standarzed because of its huge calculation amount, slow cong speed and the decong image quality needs to be improved. The next generation visual communications must address the application of capture, transmission, and splay of 3D visual information and then realize one of the most desired features of high quality telecommunication services, which is in terms of The sensation of 3D reality. Alternatively, 3D stereoscopic splays can supply the 3D representation through the human brain to fuse the left and right views of the same scene, which are captured from slightly fferent viewing angles. Undoubtedly, it will be a very attractive and effective rection and technique to realize the 3D visual communication in the near future []. The contents in the paper are organized as follows: The theory of fractal cong is summarized in section II. The proposed improving methods for fractal monocular video sequence cong are presented in section III. A detailed design of fractal stereo video sequences compression is presented in section IV. The experimental results are presented in section V. And finally the conclusions are outlined in section VI. II. CLASSICAL FRACTAL IMAGE/VIDEO CODEC Mandelbrot presented the theory of fractal at 197s [3]. Barnsley firstly used this theory to image cong [4], and Jacqain presented automatic fractal image cong method that is based on image partition and used local affine transform instead of full affine transform [1]. And then, Fisher made using of quadtree to improve the method [5]. The matching rule in fractal image cong is RMS: = RMS 1 ri s s ri o o o ri (1) ri ri () s = o = ri s r is pixel value of Range block(r), is pixel value of i Domain block(d), is the number of pixels in the block, s is the scale factor, o is the offset factor. After then, fractal cong method has been applied on video sequence compression, for instance the famous hybrid circular prection mapping (CPM) and non-contractive interframe mapping (CIM) [6]. The CPM/CIM combines fractal video cong with the well-known motion estimation and compensation (ME/MC) algorithm that exploits the high temporal correlations between adjacent frames. In CPM and CIM, each range block is motion compensated by a domain block in the previous frame, which is of the same size as the range block even though the domain block is always larger than the range block in conventional fractal image codec. The main fference between CPM and CIM is that CPM should be contractive for the iterative decong process to converge, d i /1/$5. 1 IEEE 168

2 while CIM need not be contractive since the decong depends on the already decoded frames and is non-iterative. Recently, Wang proposed a hybrid compression algorithm, which merges the advantages of a cube-based fractal compression method and a frame-based fractal compression method, and an adaptive partition instead of fixed-size partition is scussed. The adaptive partition and the hybrid compression algorithm exhibit relatively high compression ratio for the sequence of motion images from a video conference [7]. III. A OVEL FRACTAL MOOCULAR VIDEO CODEC A. Macroblock partition Macroblock partition has a large impact on calculation speed and complexity of video compression algorithm. In CPM/CIM, a frame is partitioned by quadtree partition and the iteration is used in matching process, resulting in high calculation complexity. In this paper, macroblock partition scheme like in H.64 is used. A frame is partitioned into many fixed size (generally pixels) macroblocks, and then each macroblock is partitioned as shown in Fig. 1. Mode 1 Mode Mode 3 Mode 4 Fig. 1. Microblock partition modes Fig.. Subblock partition in mode 4 Before the block matching processing, RMS of the whole microblock is calculated, and γ is defined as a threshold. The steps of macroblock partition are as following: Firstly, RMS is calculated in mode 1, if RMS is less than γ, then current IFS is saved. Otherwise RMS is calculated in mode or 3. If RMS is less than γ in mode or 3, then mode 4 is used. Especially each subblock in mode 4 can be partitioned continually until finng the matching block as shown in Fig.. The percentages of 4 modes in two video sequences are shown in Table I. TABLE I THE PERCETAGES OF FOUR MODES I TWO VIDEO SEQUECES Mode 1 Mode,3 Mode 4 foreman.cif 59.% 16.3% 4.5% news.cif 5.3% 8.6% 1.1% B. Domain block searching strategy and range The most important factor which affects the fractal compression ratio and speed is the number of the macroblock and subblock partition. The more the big size blocks, the better compression performances. As to temporal and spatial relation, the mapping block of R block is very near by the corresponng location in the reference frame, and need not to classify and rotate D block as in CPM/CIM. And the larger searching range is not helpful for the final results, so the searching range is limited from 7 to 15 pixels. C. Using homo-i-frame in H.64 The original reference frame (homo-i-frame in H.64) makes a great impact on compression ratio and decong image quality. In CPM/CIM, the original reference frames are coded by using CPM as shown in Fig. 3, and the original reference frames could be several frames. F F 1 F CPM: circular prection mapping CIM: non-contractive interframe mapping F 3 F 4 F 5 Fig. 3. The hybrid CPM and CIM cong structure But in CPM, the cong process involves complex blockclassifying, block-overturning and iteration in order to make decong frames to converge to original frames, so the compression performances are under the requirements. Then the method based on DCT which has worked effectively in JPEG image compression standard is used to treat the original reference frame. The comparison between CPM/CIM and the proposed method is shown in Table II. TABLE II THE COMPARISO OF ORIGIAL REFERECE FRAME CODIG RESULTS BETWEE CPM/CIM AD THE PROPOSED METHOD PSR/dB Compression Compression ratio time/s CPM/CIM The decrease for compression time is almost 8%, and the increase for PSR is 6.4dB, it will be very helpful for the cong process of latter frames of video sequence. D. Reducing repeating calculation The pixels summation and square summation of each searched D block are calculated accorng to Eq. 1 and Eq. when matching each R block. There are two ways to reduce repeating calculation in program: Firstly, the pixels summation and square summation relate to R block can be calculated and saved before searching, and be looked up in the searching process, thus the repeated calculation can be avoided. Secondly, the pixels summation and square summation of D block are not calculated repeatedly when matching a certain R block, but the same D block will be searched repeatedly for 169

3 adjacent R blocks. So it could reduce large calculation if the pixels summation and square summation of all D blocks are calculated before encong each frame. IV. FRACTAL STEREO VIDEO CODEC BASED O MCP AD DCP The most mature technique for the multi-view video sequences compression is the method which defined in the MPEG-4 multi-view profile [8]. With this approach, for example, the coder first compresses the left view with a monoscopic video sequence cong algorithm. To code the right view, each macroblock is prected both from the left view using sparity compensated prection (DCP), and from the previous frame of the right view using motion compensated prection (MCP) as shown in Fig. 4. To evaluate the performance of the proposed fractal monocular video codec, we use five video sequences: foreman.cif, news.cif, paris.cif, bus.cif and bridgefar.cif (35 88 pixels, 15 frames). The maximum and minimum partition block sizes are pixels and 4 4 pixels respectively. To compare the performances with other methods, H.64 (baseline profile, JM 8.6) and CPM/CIM are used. The comparison of average cong results of five video sequences is shown in Table III. The results incate that the proposed method can raise compression ratio 4 times, speed up compression time 1 times, and improve the image quality 3 to 5 db in comparison with CPM/CIM. And the PSR and compression ratio are low but near to H.64, the compression speed is better than H.64. The comparison is shown in Fig. 6 for fifteen frames of foreman.cif. As the increases, the PSR or the quality of decoded image decrease due to error cumulation. It could be resolved by inserting the I-frame PSR(dB) 3 3 CPM/CIM H.64 8 Fig. 4. MCP and DCP structure for stereo video cong MCP and DCP are used based on fractal video compression algorithm which is presented in Section III in the paper. For right view frames, the coder searches the D block in previous frame of right view using Domain block searching strategy and range in Section III (B), but for left view frames, the coder searches the D block both in right view frame (DCP) and left previous frame (MCP) as shown in Fig. 5, the block which has smaller RMS in two D blocks is the best matching block. Compression ratio (a) Comparison of PSR CPM/CIM H.64 1 (b) Comparison of compression ratio 8 Fig. 5. D block matching in MCP and DCP V. EXPERIMETAL RESULTS Compression time(s) 6 4 CPM/CIM H.64 (c) Comparison of compression time Fig. 6. The experimental comparison of foreman.cif 17

4 TABLE III THE COMPARISO OF AVERAGE CODIG RESULTS OF FIVE VIDEO SEQUECES PSR/dB Compression ratio Compression time / s CPM/CIM H.64 CPM/CIM H.64 CPM/CIM H.64 foreman.cif news.cif paris.cif bus.cif bridge-far.cif To evaluate the performance of the proposed fractal stereo video codec, we use ballroom_r.yuv and ballroom_l.yuv (64 48 pixels, 15 frames). The ballroom_r.yuv is right view, and the ballroom_l.yuv is left view. Firstly, ballroom_l.yuv is compressed by fractal monocular video codec; secondly, it is compressed by fractal stereo video codec. As shown in Fig. 7, the PSR and compression ratio of stereo codec is better than, as the calculation raises, the compression time is more than. The decoded images of 11 th frame of ballroom_r.yuv and ballroom_l.yuv are shown in Fig. 8. (a) is the original image of 11 th frame of ballroom_r.yuv ; (b) is the decoded image of 11 th frame of ballroom_r.yuv (compression ratio: 79.47, PSR: 33.5); (c) is the original image of 11 th frame of ballroom_l.yuv ; (d) is the decoded image of 11 th frame of ballroom_l.yuv (compression ratio: 73., PSR: 33.64). Compression time(s) (c) Comparison of compression time Fig. 7. The comparison of fractal monocular video codec and fractal stereo video codec of ballroom_l.yuv PSR(dB) (a) Comparison of PSR (a) Original image of 11 th frame of ballroom_r.yuv (b) Decoded image of 11 th frame of ballroom_r.yuv Compression ratio (b) Comparison of compression ratio (c) Original image of 11 th frame (b) Decoded image of 11 th frame of ballroom_l.yuv of ballroom_l.yuv Fig. 8. The decoded results of 11 th frame of ballroom_r.yuv and ballroom_l.yuv 171

5 VI. COCLUSIO Based on the classical fractal video compression method, a novel fractal monocular video compression is proposed. The improved techniques include: using more effective macroblock partition scheme instead of classical quadtree partition scheme; reducing the block searching strategy and range, thus increasing the calculation speed greatly; using homo-i-frame like in H.64; reducing repeating calculation, etc. The fractal monocular video codec uses the MCP structure. The fractal stereo video codec is proposed which each macroblock is prected both from the left view using sparity compensated prection (DCP), and from the previous frame of the right view using motion compensated prection (MCP). Experimental results incate that the proposed fractal monocular video codec can raise compression ratio 4 times, speed up compression time 1 times, and improve the image quality 3 to 5 db. As to fractal monocular video codec, the proposed fractal stereo video codec raise compression ratio 3-5 and improve the image quality.5 db. ACKOWLEDGMET The project is sponsored by the ational atural Science Foundation of China (SFC) under grant o and the Scientific Research Foundation for the Returned Overseas Chinese Scholars from the State Education Ministry of China. The authors thank for their financial supports. The authors would also like to express their appreciation to the anonymous reviewers for their insightful comments, which help improving this paper. REFERECES [1] Arnaud E. Jacquin, Image cong based on a fractal theory of iterated contractive image transformations, IEEE Transactions on Image Processing, vol. 1, no. 1, pp. 18-3, 199. [] Y. Wang, J. Ostermann and Y. Q. Zhang, Video Processing and communication, Prentice-Hall, pp ,. [3] Benoît B. Mandelbrot, The Fractal Geometry of ature, ew York: W. H. Freeman and Company, 198. [4] Michael F. Barnsley and Alan D. Sloan, A better way to compress image, Byte Magazine, vol. 13, no. 1, pp , [5] Y. Fisher, Fractal Image Compression, Fractals, vol., no. 3, pp , [6] C. S. Kim, R. C. Kim, and S. U. Lee, Fractal cong of video sequence using circular prection mapping and noncontractive interframe mapping, IEEE Transactions on Image Processing, vol. 7, no. 4, pp , [7] Meiqing Wang, and Choi-Hong Lai, A hybrid fractal video compression method, Computers & Mathematics with Applications, vol. 5, no. 3-4, pp , 5. [8] Strintzis M G, Malassiotis S, Object-based cong of stereoscopis and 3D image sequences, IEEE Signal Processing Magazine, vol. 16, no. 3, pp. 14-8,