Proceedings ofIC-NIDC2012

MODULAR DESIGN OF SC3DMC BASED ON MPEGRECONFIGURABLE GRAPHICS CODING FRAMEWORK

H · L· 1* H K· 1 S· k LIS K· 1yerm nn , yungyu nn, mwoo ee, owon im,Seungwook Lee2

, Bon-ki Koo 2, Euee S. Jang1**

'Digital Media Laboratory, Department of Electronics and Computer Engineering,Hanyang University, Seoul, Korea

2Electronics and Telecommunications Research Institute, Daejeon, Korea*starOrin@dmlab.hanyang.ac.kr, **esjang@hanyang.ac.kr

Abstract: MPEG reconfigurable media coding (RMC)is a standardization activity in MPEG to provide amodular design framework for the existing MPEGmultimedia coding standards. The objective of themodularization is to allow easy development,maintenance, and reuse of the coding tools in thestandards. We observed that the concept of RMC issuitable for the 3D graphics coding. Since there aremany overlapping object types (e.g., IndexedFaceSet inVRML, Mesh in Collada) in 3D graphics, the MPEGRMC framework can cover everything, from object typedefinition to object compression, in a modularizedapproach [1]. In this paper, we provide a walkthroughon the use of the RGC framework with the primitive­level FUs based on existing graphics coding standardssuch as MPEG scalable complexity 3D mesh coding(SC3DMC).

Keywords: Reconfigurable Graphics Coding; ScalableComplexity 3D Mesh Coding; primitive-level FU

1 IntroductionReconfigurable media coding (RMC), known previouslyas reconfigurable video coding (RVC), is one of the

recent standardization activities in ISO/IEC MPEG. TheMPEG RMC framework is to provide the tool-levelspecification of the standard multimedia codecs. Thetools in the RMC framework is called functional unit(FU), and a set of the FUs in the RMC framework isreferred to as tool library.

In the RMC framework, a multimedia decoder can becomposed from the FUs chosen from the tool library byinterconnecting them. Figure 1 shows a conceptual flowof the decoder configuration in the RMC framework [2].To compose the decoder, the RMC framework needs torefer auxiliary information called decoder description.Decoder description consists of two parts: FU networkdescription (FND) and bitstream syntax description(BSD). FND contains the description of the connectionlinks by represented FU network language (FNL) basedon extensible markup language (XML) between FUs inthe configured decoder. BSD is a set of bitstreamparsing instructions, which allows the RMC frameworkto customize bitstream parsing procedure for theconfigured decoder.

Figure 1 Conceptual diagram of the RMC framework

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Based on the RMC framework, MPEG is currentlydeveloping the reconfigurable graphics coding (RGC)standard. In RGC, 3D graphics codecs are representedwith several FUs which are designed to support variousgraphics primitives. Because 3D graphics objects arerepresented as a combination of the primitives, it can beexpected that some primitive-level FUs can be reusableover different object representations. Therefore, theprimitive-level design of FUs in RGC can result in anefficient design of decoder.

In this paper, we present how the aforementionedprimitive-level FUs can be designed through thepractical examples of FUs based on MPEG scalablecomplexity 3D mesh coding (SC3DMC). The rest ofthis paper is organized as follows: in Section 2, wereviewed feature of SC3DMC, which was mainly using3D mesh compression schemes in this paper. Wedefined modular design of SC3DMC decoder based onthe existing SC3DMC reference software in Section 3.We also described FU networks and each FUs of theproposed modular design in Section 3. Finally, weconclude this paper in Section 4.

2 Features of SC3DMC

SC3DMC is one of 3D mesh compression standardsdeveloped by MPEG 3D graphics coding (3DGC)subgroup [3]. The SC3DMC toolset specifies how toexact modulate the trade-off compression efficiency(bpv) against computational resources (CPU andmemory) by selected 3D mesh coding techniques. Thereare three parameterized 3D mesh coding methods inSC3DMC: quantization based compact representation(QBCR), shared vertex analysis (SVA) and triangle fan­based compression (TFAN) [4].

Among low-complexity 3D mesh compression modelschemes, QBCR is the simple and fast design because itonly uses quantization and inverse quantization [3]. InSVA, a vertex may be used by several faces. Theconnectivity between current face index and previousface index is investigated for a number of sharedvertices. By the number of shared vertices, current facevertex index uses different computing method of thedifference in the vertex index (DVI) [5]. TFAN codingalgorithms is based on the triangles to consist of vertices.The useful point of TFAN concept is to representconcisely the connectivity between vertices [6].

3 Modular Design of SC3DMC

In this section, we discuss how the aforementionedSC3DMC codec can be represented with a set of FUs.As shown in Figure 2 and Figure 3, the practical FUnetworks are illustrated based on QBCR which is one ofthe three coding methods in MPEG SC3DMC [7]. Forthe coordinate decoding, FUs are connected in thefollowing order: syntax parser FU, inverse predictionFU, inverse quantization FU, and composer componentFU.

In general, the coordinate is composed of the one ormore dimension data and they are independentlycompressed without using any correlation between them.Therefore, the decoder can be configured using FUs foreither one-dimensional processing or multi -dimensional(i.e., n-dimensional) processing. Figure 2 and Figure 3shows the FU networks based on the FU designs forone-dimensional and n-dimensional processing,respectively. The n-dimensional FU network can beparameterized by the RMC framework with the specificdimension that the decoder will process.

Figure 2 FU network for coordinate coding based on one-dimension

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Figure 3 FU network for coordinate coding based on n-dimension

Mode Calculation

Table I Calculations by prediction modes

dataOut[j] = dataln [j],NP \:j j E {O.. N - 1}

{

DataIn[j], \:j j = 0Diff dataOut[j] = DataIn[j] + DataOut[j - 1],

\:j j E {1 .. N - 1}

dataOutU]

{

dataIn[j],if ditValue[j] = 0 or j = 0

= dataIn[j] + dataOutj] - difvaluejjj],otherwise

{

dataIn[j], \:j j = 0dataOut[j] = dataIn[j] Q9 dataOut[j - 1],

\:j j E {1 .. N - 1}

{

dataln[j], \:j j = 0

dataOutU] = if data/nUl ~'data/nU - 1]-d, otherwise

{

dat aIn [j - 1] + Md - dataIn[j],d = if dataIn[j] > dataIn[j - 1]

dataIn[j] + Md - dataIn[j - 1],otherwise

XOR

Circular

Adaptive

Figure 5 shows the process schematic of the n­dimensional IP FU. It can be shown that output data iscalculated differently by the prediction mode (i.e., non­prediction (NP), differential prediction (Diff), exclusiveprediction (XOR), adaptive differential prediction(Adaptive), and circular prediction (Circular)) [8]. Thespecific calculation formulas for each mode arepresented in Table I [9].

3.1 Generic Parser FU (GPFU)

In the RMC framework, it is expected that a bitstreamparser FU supplies parsed data to the FUs in the rest ofthe FU network. In our modular design of SC3DMC,generic parser FU (GPFU) is used as the bitstreamparser FU [2]. A key feature of GPFU is its run-timeconfigurability: GPFU receives BSD as an input in run­time and dynamically configure bitstream parsingprocess according to the BSD. In other words, thebitstream parsing process can be customized in run-time.The run-time configurability of GPFU allows theparsing of various object types (e.g., VRML orCOLLADA format) into a set of graphics primitives bya single pre-defined parser FU. This feature helps theother FUs designed at primitive-level to perform itsdecoding process regardless of how the primitive wascoded in 0 bject.

3.2 Inverse Prediction (IP) FU for QBCR

As previously stated in the description of the FUnetworks, the FUs are designed to be used in both of theone-dimensional and n-dimensional FU networks.Figure 4 shows the block diagram about input/outputdata of inverse prediction (IP) FU. Three input data arecoordinate or index data, difference value, andprediction mode. An output data is the result of inverseprediction. Using the same FU, n-dimensionalprocessing can be triggered by an FU parameter, dimD.The parameter can be set at the network configurationlevel [8].

Figure 4 Inverse Prediction FU block diagram

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Figure 5 Inverse Prediction FU process schematic based on n-dimension

3.3 Inverse Quantization (IQ) FU for QBCR

Both one-dimensional IP FU and n-dimensional IP FUhave five input data and one output data, as shown inFigure 6. As n-dimensional IP FU, n-dimensionalinverse quantization (IQ) FU also requires parameters,dimD and homogeneousQ, to determine its workingdimension. Figure 7 shows the IQ FU process schematicbased on n-dimension. The output data is calculatedusing the following formula [8-9]:

dataOutUJ == quantMin + datalnUJquantRange

x ( 2quantValue - 1)

Figure 6 Inverse Quantization FU block diagram

Figure 7 Inverse Quantization FU process schematic based on n-dimension

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4 Conclusions

In this paper, we presented FU networks and primitive­level FUs for QBCR in SC3DMC. The FUs aredesigned to be reusable over different objectrepresentations. We also shown that the FU design arecapable of both one-dimensional and n-dimensional FUnetwork composition. Because 3D graphics objects arerepresented as a combination of the primitives, thepresented FU design can offer more efficientimplementation of SC3DMC decoder. As a furtherextension of our initial approach on 3D graphics FUdesign, we are continuing the study on the FU designprinciples for various 3D graphics objects.

Acknowledgements

This work was supported by the strategic technologydevelopment program of MCST/MKE/KEIT.[KI001798, Development of Full 3D ReconstructionTechnology for Broadcasting Communication Fusion]

References[1] Sinwook Lee, Taehee Lim, Ji Hyung Lee, Seungwook

Lee, Euee S. Jang, "MPEG Reconfigurable GraphicsCoding Framework: Overview and Applications",Visual Communications and Image Processing (VCIP),November 2011.

[2] Hyungyu Kim, Sowon Kim, Seungwook Lee, Euee S.Jang, "Parser Description-based Bitstream ParserGeneration for MPEG RMC Framework", Signal

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Processing: Image Communication, Paper under review.[3] ISO/IEC JTCl/SC29/WGll, ISO/IEC 14496-16 AMD 4

Scalable Complexity 3D Mesh Coding, MPEG­document ISO/IEC JTC1/SC29/WG11 Nl 0018, MovingPicture Experts Group (MPEG), Hannover, Germany,July 2008.

[4] Francisco Moran Burgos and Marius Preda, "MPEG 3DGraphics Representation", The MPEG Representation ofDigital Media, Chapter 11.

[5] Euee Seon Jang, Seungwook Lee, Bonki Koo, DaiyongKim, Kyoungsoo Son, "Fast 3D Mesh CompressionUsing Shared Vertex Analysis", ETRI Journal, vol. 32,No.1, February 2010.

[6] Khaled Mamou, Titus Zaharia, Francoise Preteux,"TFAN: A low complexity 3D mesh compressionalgorithm", Computer Animation and Virtual Worlds,vol. 20, Issue 2-3, pages 343-354, June 2009.

[7] Seungwook Lee, Ji Hyeong Lee, Hyerin Lim, SinwookLee, Taehee Lim, Bonki Koo and Euee S. Jang,"Contribution of SC3DMC FUs for RGC framework",MPEG-document ISO/IEC JTCl/SC29/WGll M2l220,Moving Picture Experts Group (MPEG), Torino, Italy,July 2011.

[8] ISO/IEC JTC1/SC29/WG11, ISO/IEC 23002-4:20l0/PDAM 3 Graphics Tool Library forReconfigurable Multimedia Coding Framework, MPEG­document ISO/IEC JTCl/SC29/WGll N12589, MovingPicture Experts Group (MPEG), San Jose, USA,February 2012.

[9] ISO/IEC JTC1/SC29/WG11, ISO/IEC 23002-4 Amd. 3Graphics Tool Library for Reconfigurable MultimediaCoding Framework, MPEG-document ISO/IECJTCl/SC29/WG11 N12426, Moving Picture ExpertsGroup (MPEG), Geneva, Switzerland, November 2011.