Alberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis Martínez, Gerardo Fernández-Escribano, José M. Claver and José Luis Sánchez

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1 Alberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis artínez, Gerardo Fernández-Escribano, José. Claver and José Luis Sánchez

2 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC GPU-based Video Transcoder 4. Results 5. Conclusions 2

3 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC GPU-based Video Transcoder 4. Results 5. Conclusions 3

4 Traditional coding: encoders more complex than decoders. Tipical scene: digital television. 4

5 Recently, new applications have been introduced. These applications have few resources. Examples: Surveillance Wireless etworks, Sensors etworks, icro cameras, obile Devices or PDAs. Devices which need low consumption and low complexity ain idea of Distributed Video Coding (DVC): the complexity is displaced from the encoder to the decoder The encoder avoids the temporal correlation. Some frames are not transmitted by the encoder. These frames are estimated in the decoder side : Estimation with available frames (Side Information) + Correlation odel + Parity Bits 5

6 Traditional coding : H.264 Encoder with high complexity Decoder with low complexity DVC: Encoder with low complexity Decoder with high complexity Idea: Taking advance of the low complexity of both paradigms for mobile-to-mobile video communications Disadvantage: The transcoder joins the highest complexity of both paradigms ETWORK DVC Encoder (low complexity) H.264 Decoder (low complexity) DVC stream ube H.264 stream DVC to H.264 Transcoder (high complexity) 6

7 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC GPU-based Video Transcoder 4. Results 5. Conclusions 7

8 DVC encoder: K WZ K WZ K Key Frames Wyner-Ziv frames Parity Bits DVC decoder: Side Information otion Vectors (Vs) Search area Frame k V SI Frame k+n Lowest Residual B B interpolated B source 8

9 A Graphic Processing Unit, or GPU, is a specialized processor that was originally designed for offloading 3D graphics rendering from the microprocessor. Recently, nvidia has developed a powerful GPU architecture denominated Compute Unified Device Architecture (CUDA), which is accessible to software developers through industry standard programming languages, such us C, Python, Fortran, Java and atlab. Hence, the E algorithm developed in the H.264/AVC encoding algorithm fits well in the GPU philosophy, and offers us a new challenge for the GPUs. The main goal is how to efficiently distribute all the computations over the GPU and how DVC can improve the process d

10 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC GPU-based Video Transcoder 4. Results 5. Conclusions 10

11 Key Frames Bitstream Regular Intra- Frame Decoder X 2i-1 X 2i+1 Side Information Generation Y 2i DCT REGULAR CODED BITSTREA H.264 I Frames Parity bitstream Turbo Decoder Correlation oise odeling X q 2i 2i Reconstruction IDCT Vs WYER-ZIV - DECODER F n (current) E + S - GPU D n X T Q Reorder Entropy encode AL F n-1 (reference) C Inter P Intra prediction Intra F n (reconstructed) Deblocking Filter uf n S + + D n T -1 Q -1 H.264 ECODER 11

12 H.264 otion Estimation Reference Frame Current Frame Original Search Area Search area Predictors B Predictors are replaced by DVC Vs 1. Sum Absolute Differences (SAD) calculation between the current B (split into sixteen 4x4 partitions) 2. SAD costs for the different sub-partitions 3. SAD reduction cost to one SAD cost for each one 12

13 apping otion Vectors (GOP 2) K WZ K WZ K DVC I I I P P P P H

14 apping otion Vectors (GOP 4) K WZ WZ WZ K DVC K WZ WZ WZ I DVC I P P P P H.264 The process can be extended for all DVC GOPs 14

15 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC GPU-based Video Transcoder 4. Results 5. Conclusions 15

16 Simulation conditions The decoder was implemented with VISET-II. The encoder was implemented using the H.264 J reference software (J15.1). The approach is compared with an unmodified H.264 J reference software encoder QP values 28, 32, 36 and 40 were use for testing the sequences. GOP format: DVC: 2, 4 and 8 H.264: I11(P) Feature GTX285 Compute capability 1.3 Global emory 1 GB umber of multiprocessors 30 umber of cores 240 Constant memory 64 KB Shared memory per block Registers per block ax. active threads per multiprocessor Clock rate 16 KB GHZ Tested Sequences: Foreman, CoastGuard, Hall and Soccer at 30 fps

17 RD performance of the WZ/H.264/AVC video transcoder 30fps Sequence GOP PSR (db) Bitrate (%) TR (%) fps ,12 Foreman , , ,96 Hall , , ,11 Coastguard , , ,57 Soccer , ,65 mean ,44 17

18 PSR RD performance for QCIF sequences Sequences QCIF (176x144) 30 fps GOP = Hall Soccer CoastGuard Foreman Reference Proposed Bit rate [kbit/s] 18

19 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC GPU-based Video Transcoder 4. Results 5. Conclusions 19

20 DVC to H.264 transcoding provides a suitable framework to support mobile-to-mobile video communications. However, the transcoder has high complexity and it should be reduced. otion Estimation is the most complex task of H.264 and GPUs can help to accelerate it by using Vs generated by DVC. Experiments show that the complexity of E is reduced about a 79% without significant RD penalty. This proposal provides a first step in DVC to H.264 parallel GPU-based transcoding. 20

21 Any question? DVC: GPU/H.264: 21

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