高攀

Associate Professor   Supervisor of Master's Candidates

Gender:Male

Education Level:澳大利亚南昆士兰大学

Degree:Doctoral Degree in Engineering

School/Department:College of Computer Science and Technology

Discipline:计算机科学与技术

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Paper Publications

Occlusion-Aware Depth Map Coding Optimization Using Allowable Depth Map Distortions

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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:IEEE Trans Image Process

Abstract:In depth map coding, rate-distortion optimization for those pixels that will cause occlusion in view synthesis is a rather challenging task, since the synthesis distortion estimation is complicated by the warping competition and the occlusion order can be easily changed by the adopted optimization strategy. In this paper, an efficient depth map coding approach using allowable depth map distortions is proposed for occlusion-inducing pixels. First, we derive the range of allowable depth level change for both the zero disparity error case and non-zero disparity error case with theoretic and geometrical proofs. Then, we formulate the problem of optimally selecting the depth distortion within allowable depth distortion range with the objective to minimize the overall synthesis distortion involved in the occlusion. The unicity and occlusion order invariance properties of allowable depth distortion range is demonstrated. Finally, we propose a dynamic programming based algorithm to locate the optimal depth distortion for each pixel. Simulation results illustrate the performance improvement of the proposed algorithm over the other state-of-the-art depth map coding optimization schemes. © 1992-2012 IEEE.

ISSN No.:1057-7149

Translation or Not:no

Date of Publication:2019-11-01

Co-author:Smolic, Aljosa

Correspondence Author:高攀

Pre One:Texture-Distortion-Constrained Joint Source-Channel Coding of Multi-View Video plus Depth-Based 3D Video

Next One:Optimization of Occlusion-Inducing Depth Pixels in 3-D Video Coding

Profile

博士毕业于澳大利亚南昆士兰大学。2018年在爱尔兰都柏林圣三一学院担任研究员,师从视觉计算领域顶级专家Prof. Aljosa Smolic。主要研究方向为图像处理、计算机视觉、深度学习以及生成式人工智能(AIGC)等,并在这些方向取得了一系列的研究成果。


在国际知名刊物IEEE TIP, TMM, TCSVT, TBC, PR等以及知名会议CVPR, NeurIPS, ECCV, AAAI, IJCAI, ACM MM, ICIP, ICASSP等发表高水平论文80余篇。其中,发表在图像处理领域顶刊IEEE Trans. On Image Processing的一项关于深度图处理与优化方面的研究成果,由于其从理论上证明了深度图中可容忍失真度的界限,打破了该领域内长期认为遮挡像素对编码无用而被舍弃的思想,被遴选为该刊的亮点文章(该刊物每年评选不超过10篇论文作为亮点论文进行报道)。另外,发表在多媒体领域顶刊IEEE Trans. On Multimedia 的一篇关于全方位全景视频质量评价与编码的论文,由于其巧妙地解决了困扰该领域多年的VR视频的时域质量评测问题,被受邀在国际电气与电子工程师学会的信号处理学会作线上报告(IEEE SPS Webinar链接)。该报告吸引了数百名国内外学者参与。


目前为CCF多媒体专委会执行委员,CSIG多媒体专委会委员。担任多个SCI国际期刊(如:Frontiers in Signal Processing)的编委和多个人工智能领域顶会如IJCAI、AAAI、ACM MM的程序委员会委员。获得华为智能基座产教融合协同育人基地奖,指导本科生在计算机视觉领域顶会CVPR发表论文(相关报道),获得RoboCup机器人大赛二、三等奖,全球校园人工智能算法精英大赛三等奖等。申请国家发明专利20余项,并获得授权发明专利10余项,实现专利权转让1项,拥有软件著作权4项。


欢迎"三好"学生(编程好、英语好、数学好),并且有志于从事学术科研的同学报考(寻找能看能写能实现英文文献的你)。鼓励学生学术创新,发表顶会论文,以及出国交流等。指导的学生在CCF A类顶会、IEEE Transactions上发表论文,多名研究生获得国家奖学金、华为奖学金等。

(持续招收2025届研究生,欢迎邮件联系!)


更多信息,请访问实验室主页:https://i2-multimedia-lab.github.io/ 和公众号: I2ML