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학술지 Nonlinear Depth Quantization Using Piecewise Linear Scaling for Immersive Video Coding
Cited 7 time in scopus Download 49 time Share share facebook twitter linkedin kakaostory
박도현, 임성균, 오관정, 이광순, 김재곤
IEEE Access, v.10, pp.4483-4494
22GH1300, [전문연구실] 이머시브 미디어 전문연구실, 서정일
Moving Picture Experts Group (MPEG) is developing a standard for immersive video coding called MPEG Immersive Video (MIV) and is releasing a reference software called Test Model for Immersive Video (TMIV) in the standardization process. The TMIV efficiently compresses an immersive video comprising a set of texture and depth views acquired using multiple cameras within a limited 3D viewing space. Moreover, it affords a rendered view of an arbitrary view position and orientation with six degrees of freedom. However, the existing depth quantization applied to depth atlas in TMIV is insufficient since the reconstructed depth is crucial for achieving the required quality of a rendered viewport. To address this issue, we propose a nonlinear depth quantization method that allocates more codewords to a depth subrange with a higher occurrence of depth values located at edge regions, which are important in terms of the rendered view quality. We implement the proposed nonlinear quantization based on piecewise linear scaling considering the computational complexity and bitstream overhead. The experimental results show that the proposed method yields PSNR-based Bjontegaard delta rate gains of 5.2% and 4.9% in the end-to-end performance for High- A nd Low-bitrate ranges, respectively. Moreover, subjective quality improvement is mainly observed at the object boundaries of the rendered viewport. The proposed nonlinear quantization method has been adopted into the TMIV as a candidate standard technology for the next MIV edition.
KSP 제안 키워드
Computational complexity, Degrees of freedom(DOF), End to End(E2E), End-to-end performance, Immersive video, Linear scaling, Moving picture experts group(MPEG), Position and orientation, Quality improvement, Six degrees of freedom(6-DoF), Subjective quality
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저작자 표시 - 비영리 - 변경금지 (CC BY NC ND)