A comparison of two parallel algorithms using predictive load balancing for video compression


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1 A comparison of two parallel algorithms using predictive load balancing for video compression CARLOSJULIAN GENISTRIANA 1, ABELARDO RODRIGUEZLEON 2 and RAFAEL RIVERA LOPEZ 2 Departamento de Sistemas y Computación Instituto Tecnológico de Veracruz Calzada M. A. de Quevedro 2779, Col. Formando Hogar, Veracruz, Ver. MÉXICO 1 2 Abstract: This paper shows a comparison of two parallel algorithms for video compression that use predictive load balancing. One algorithm uses an exhaustive estimation of movements and the other one has an adaptive scheme. These algorithms are based on the H.264 standard of video compression and they are constructed using a Group of Pictures (GOP) level of parallelization. These algorithms are evaluated to determine their speedup when they are executed with a different number of nodes in a cluster processing videos of different types and resolutions. This evaluation is used for determining the equation that is used for calculating the number of nodes required to obtain a video compression in real time. KeyWords:  parallel programming, load balancing, video compression. 1 Introduction In recent years, several standards for video compression have been developed (H.264 [1], MPEG4 [2], JPEG2000 [3]), each one with advantages and drawbacks. The main challenge for video compression is to reduce the size of the transmitted video without to loss its quality. Video compression requires high performance systems that permits to reduce the compressiontime, and it is in this moment that the multiprocessor systems (tightlycoupled or looselycoupled) are required. The use of these systems (clusters by example) for video compression requires the implementation of applications using parallel programming [4]. There are several approaches for parallel programing, but Message Passing Interface (MPI) [5] is a de facto standard for communication among processes that model a parallel program running on a distributed memory system. Digital video consists of a sequence of images (frames) and the effect of motion (small and continuous changes) between frames. A sequence of frames is called a "group of pictures" (GOP). In video compression, GOPs are processed for removing the similarities between consecutive video frames or for reducing irrelevant and redundant information in frames. This redundant information is deleted for the application of predictive models, sensory redundancy or statistic redundancy [6]. Predictive model uses two methods for frame codification: intraframe, when pixels in a frame are encoded using only the information of this frame, and interframe, when a frame is encoded using the information of contiguous frames. Frames encoded using infraframe codification are called Iframes, frames that are encoded using only a previously displayed reference frame are called Pframes and frames that are encoded using both future and previously displayed reference frames are called Bframes. This paper focuses on developing a comparison between two parallel algorithms for the video compression that use predictive load balancing. These algorithms are based on the H.264 standard of video compression and they are constructed using a parallelization of GOPs. Load distribution used in these algorithms uses two schemes: First, a preassignment scheme is applied for the initial allocation of GOPs in nodes of a cluster, then, an ondemand distribution scheme is applied, constructing a schedule based on the complexity analysis of encoding the next GOPs. This schedule sends a heavy GOP to the node that previously codified a lighter GOP. These algorithms are predictive algorithms with motion estimation. One algorithm uses an exhaustive estimation of movements and the other one has an adaptive scheme. These algorithms are evaluated to determine their speedup when they are executed with a different number of nodes of a cluster processing videos of different types and resolutions. This evaluation is used for determining the equation that is used for calculating the number of nodes required to obtain a video compression in real time. ISBN:
2 The rest of this paper is organized as follows. In the next section, we describe the predictive load balancing schemes used for these algorithms. Section 3 includes a description of the predictive parallel algorithm with exhaustive motion estimation. Section 4 describes the predictive parallel algorithm with adaptive motion estimation. Finally, we summarize the results of this work in Section 5. 2 Predictive load balancing schemes The parallelization approach defined in this work uses a video stream that is divided in GOPs of 15 frames. In this approach, a GOP has the pattern IBBPBBPBBPBBP [7]. In this pattern, a Iframe indicates the start of a new GOP in the video sequence. The use of this pattern avoids the loss of quality produced in an interframe codification and it permits that each GOP can be encoded in different nodes of a cluster. 2.1 Preassigment scheme This scheme is used to assign the first group of GOPs to the nodes in a cluster. The computational cost for encoding frames is variable and unpredictable. For a cluster with n_proc processors, each processor is identified by a number between 0 and n_proc1. The number of GOPs (n_gops) is divided by n_proc, and this value is identify as n_gop_v. Each processor receives n_gop_v GOPs for encoding. If n_gops is not multiple of n_proc, the remaining GOPs (n) are assigned to first n processors. Figure 1 shows a state graph for preassigment scheme. Table 1 shows a description of transitions in figure 1. Fig. 1. Preassigment scheme for GOPs. 2.2 Ondemand distribution scheme In an ondemand distribution scheme, the nodes of the cluster receive the next GOP when it has finished its compression. In this scheme, a dealernode is selected for coordinating the GOP assignment to the free nodes (encodernodes). The objective of this scheme is to reduce the idle time in cluster processors. Table 1. Transitions in preassigment scheme. Transition Description T.1.1,, T.N.1 Processor 1,, N calculates the GOP to encode. T.1.2,, T.N.2 Processor 1,, N reads the GOP of disk. T.1.3,, T.N.3 Processor 1,, N encodes the GOP. T.1.4,, T.N.4 Processor 1,, N writes the encoded GOP. In figure 2 is showed the algorithm used for this distribution scheme. Figure 3 shows a graph for the ondemand distribution scheme and table 3 shows a description of the transitions in figure 3. procedure on_demand_distribution{ for(n_gop = 0 to n_gops){ if(n_gop < n_proc) proc = n_gop; else proc = wait_for_processor(); Gop_codification(proc,n_Gop); } halt_all_processors(); write_codified_gops(); } Fig. 2. GOPs ondemand distribution algorithm. Fig. 3. GOPs ondemand distribution scheme. 3 Predictive parallel algorithm with exhaustive motion estimation This algorithm distributes the GOPs using a prediction process applied in each encodernode. This algorithm uses an exhaustive motion estimation because it searches for blocks with all possible sizes used for the compression process. Table 2. Transitions in ondemand distribution scheme. Transition Description T.1.0,, T.N.0 Processor 1,, N notifies that is available. T.1.1,, T.N.1 Processor 1,, N determines the GOP to encode. T.1.2,, T.N.2 Processor 0 notifies to processor 1,, N the number GOP to be encoded. T.1.3,, T.N.3 Processor 1,, N reads the GOP of disk. T.1.4,, T.N.4 Processor 1,, N encodes the GOP. T.1.5,, T.N.5 Processor 1,, N writes the encoded GOP. ISBN:
3 In this algorithm, the dealernode (processor 0) assigns the GOPs to the encodernodes (processors 1,, N). Encodernodes read of disk the GOP assigned and starts the compression process. Before of to finish this compression process, each encodernode sends to the dealernode one estimatedtime for to finish its compression process. Just before to finish the compression process in encodernodes, dealernode sends the next GOP number to be codified at each encodernode, with the aim of to reduce the idle time. This algorithm defines two elements for this prediction process: the GOPwindow and the prediction scheme for the compressiontime. 3.1 GOPwindow A GOPwindow is the size of a group of GOPs in relation with the number of encodernodes. In (1) is defined the value of GOPwindow (n_gop_v) when n_gops is the number of GOPs in the video stream and n_proc is the number of encodernodes in a cluster. (1) Each encodernode is assigned to one GOPwindow. In each step of codification process, the encodernodes are interchanged into the GOPwindows. The load balancing is obtained due that the allocation of encodernode to the GOPwindow is based on the computational cost of the GOP compressiontime. In figure 4 is showed an example with three iterations of the assignment process, using nine GOPs grouped in three GOPwindows and using three encodernodes. Fig. 4. A example with three rounds for GOPwindow assignment. In this example, for the first round of assignment, the heavier GOPwindow is the number 2, and the lightest GOPwindow is the number 1. When the first round is finished, the encodernodes 1 and 2 are swapped in their GOPwindows. In the second round of assignment, the heavier GOPwindow is the number 3, and the lightest GOPwindow is the number 2. When this round is finished, the encodernodes 1 and 3 are swapped in their GOPwindows. 3.2 Prediction scheme of the compressiontime For predicting the compressiontime of one GOP it is necessary to use 16 frames of video stream, but only 15 frames will be stored. Frame 16 is necessary because the frame 15 is a Bframe and its uses both future and previously frames into video stream in its compression process. Iframe only uses one frame in its compression, and Pframe uses two frames in its compression. Figure 5 shows the compression sequence for the IBBPBBPBBPBBPI pattern. Sequence Frame Type I P B B P B B P B B P B B I B B Frame Number in GOP Fig. 5. Compression sequence of 16 frames. The prediction scheme is based on sending two messages to the dealernode: Estimatedtime message: The estimatedtime is calculate using the average compression time of each type of frame encoded in a GOP. In the compression process, when the frame 12 is encoded, the estimatedtime is calculated. The estimatedtime is defined in (2). t est = t I avgi r + t P avgp r + t B avgb r (2) In (2), t est is the estimated time, t I avg is the average compression time for Iframes, I r is the number of Iframes uncoded, t P avg is the average compression time for Pframes, P r is the number of Pframes uncoded, t B avg is the average compression time for Bframes, and B r is the number of Bframes uncoded. Finishcompression message: When the encodernode has processed the frame 15, the dealernode is notifed that is finished. Then, the dealernode identifies the more complex GOPwindow and a encodernode is assigned. Dealernode receives the estimatedtime of each ISBN:
4 encodernode, the shortest estimatedtime received is used by the dealernode as a timeout for starting the scheduling process. This scheduling process assigns the encodernodes to the GOPwindows using the estimatedtimes received. Then the encodernode that finished first is assigned to the heavier GOPwindow. Figure 6 shows a graph for predictive parallel algorithm with exhaustive motion estimation and table 4 shows a description of transitions in figure 6.  E2: Search using only 16x16 blocks. In a lowmotion video, a 16x16 block is sufficient for encoding the video without quality loss. In other hand, in an highmotion video, it is necessary to explore with several blocks sizes. When a video stream is compressed using few blocks, the compression process is faster but the main problem is to define, a priori, the motion conditions in a video stream. In [8] is defined a adaptive level, identified as EA, that uses a manual assignment of precision levels. This algorithm was tested using two video streams (Foreman CIF and Stockholm HDTV). In figures 7, 8 and 9 are showed the comparison of compressiontime, qualityloss and bitrate increment for these adaptive levels. Fig. 6. Predictive parallel scheme with exhaustive motion estimation. Table 4. Transitions in predictive parallel scheme with exhaustive motion estimation. Transition Description T.0 Processor 0 defines the GOP assignment of first round. T.1.0,..., T.N.0 Processor 0 notifies to processor N the number of GOP to encode. T.1.1,, T.N.1 Processor 1,, N reads the GOP of disk. T.1.2,, T.N.2 Processor 1,, N encodes the GOP. T.1.3,, T.N.3 Processor 1,..., N sends its predictiontime. T.1.4,, T.N.4 Processor 0 stores the predictiontime. T.1.5 Processor 0 sets the Timer T.M Timer starts the scheduling. T.0 Processor 0 defines the GOP assignment of second round. T.1.6,, T.N.6 Processor 1,, N writes the encoded GOP. 4 Predictive parallel algorithm with adaptive motion estimation This algorithm is designed for reducing the computational time of the motion estimation [x8]. H.264 standard permits to define the characteristics of motion estimation using several parameters. Using these parameters it is possible to define several precision levels. In this case, three levels are used:  E0: Exhaustive search.  E1:Search using 16x16, 16x8, 8x16 and 8x8 blocks. Fig. 7. Comparison of compression time. Fig. 8. Comparison of quality loss. Fig. 9. Comparison of bit rate increment. ISBN:
5 These results suggest that the use of an adaptive motion estimation is one interesting approach because it reduces the compression time (15%), and the qualityloss and the bitrate increment are comparable with the other approaches. 5 Performance analysis Tests of these predictive parallel algorithms were realized in the Mozart cluster. Mozart has 4 biprocessor nodes with AMD Opteron 246 at 2 GHz interconnected by a switched Gigabit Ethernet. The video sequence used is the Riverbed HDTV. The test sequences of Riverbed have three image resolutions: 720x480, 1280x720 and 1920x1088. Each algorithm is evaluated using 2, 4 and 8 processors and their efficiency is determined (table 5). In this table, it is observed that the average efficiency is similar for all video resolutions and all number of processors. To estimate the number of processors required for realtime encoding, the average efficiency (0.9524) obtained in this analysis is used. Table 5. Efficiency of predictive algorithm. Resolution 2P 4P 8P Average 720x x x Average To achieve realtime encoding in a cluster is necessary that the encoded GOPs per second is comparable with the GOPs per second generated by the video source. The estimation of number of processor is based on the aplication of Little's law [9], showed in equation (3). In this formulation, a job is the process of to encode one GOP in one processor. The equation terms are: N = X*R (3) N: Number of GOPs processed in a cluster, number of processors. R: Compressiontime of a single GOP in a cluster. X: Number of GOPs encoded per second (compression productivity). In this analysis, is the parallel compression time, R SEC is the sequential compression time, L is the number of GOPs to be encoded, Sp is the SpeedUp, E G is the efficiency. Then, the parallel compression productivity is defined in (4), the sequential productivity is showed in (5), the speedup is defined in (6) and the efficiency in (7). Sp= L X SEC = L E G = Sp N = = N (4) X SEC = 1 R SEC (5) X SEQ = N R SEQ N N 1 R SEQ = N R SEQ (6) = R SEQ (7) Using equation (7), the parallel compression time is defined in (8) and, using the Little's law, the number of processors is defined in (9). N = = R SEQ E G (8) = R SEQ E G (9) For to achieve realtime compression are needed to encode 30 frames per second (2 GOPs per second). If, by example, the sequential compression time (R SEC ) for encoding 15 frames with resolution of 720x480 is seconds, and it is used the average efficiency for this resolution in table 5 (E G = 0.95), the number of processors required is : N = = 531 processors Conclusions In this paper is presented a comparison between two parallel algorithms using predictive load balancing for video compression, the adaptive approach reduces 15% the compressiontime in relation with the algorithm with exhaustive estimation of movements. The principal drawback of adaptive motion estimation is the complexity of determining the block size to ensure a little visible quality loss based on the conditions of movement of the video stream. Using the Little's law, it is possible to determine the number of processor required for to produce real time compression. As future work, it is necessary to realize more exhaustive tests for to determine the scalability of the algorithm. ISBN:
6 References: [1] T. Wiegand, G.J. Sullivan, G. Bjontegaard and A. Luthra: Overview of the H.264 / AVC Video Coding Standard, in IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, no. 7, 2003, pp [2] T. Ebrahimi, C. Horne: MPEG4 natural video coding  An overview, in Signal Processing: Image Communication, 15, 2000, pp [3] M. J. Gormish, D. Lee, and M.W. Marcellin: JPEG 2000: Overview, architecture and applications, in Proc. IEEE Int. Conf. Image Processing, Vancouver, Canada, 2000, vol. II, pp [4] J.C. Fernandez, M.P. Malumbres: A parallel implementation of H.26L video encoder, in Lectures Notes on Computer Sciencies, vol. 2400, pp , [5] D. W. Walker: The Design of a Standard Message Passing Interface for Distributed Memory Concurrent Computers, in Parallel Computing, vol. 20, no. 4, pp , [6] I. E. G. Richardson: Video Coding Design, Wiley, [7] A. Rodriguez, A. González, M.P. Malumbres.: Diseño de un Algoritmo Paralelo para codificación de Vídeo MPEG4 sobre un cluster de Computadoras Personales, in Memorias del 9o. Congreso Internacional de Investigación en Ciencias Computacionales, Puebla, Mexico, pp , [8] A. RodriguezLeon, M. Perez, A. Gonzalez, J. Peinado, J.C. Fernandez: Paralelización del codificador H.264 con estimación de movimiento adaptiva en clusters de PCs, in Actas de las XVI Jornadas de Paralelismo, JP2005, pp , [9] J. D. C. Little: A Proof of the Queuing Formula: L =λw, in Operations Research, Vol. 9, No. 3, pp , ISBN:
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