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Compression scheme for sparsely sampled light field data based on pseudo multi-view sequences
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2018 (English)In: SPIE Photonics Europe 2018: Proceeding, 2018Conference paper, Published paper (Refereed)
Abstract [en]

With the advent of light field acquisition technologies, the captured information of the scene is enriched by having both angular and spatial information. The captured information provides additional capabilities in the post processing stage, e.g. refocusing, 3D scene reconstruction, synthetic aperture etc. Light field capturing devices are classified in two categories. In the first category, a single plenoptic camera is used to capture a densely sampled light field, and in second category, multiple traditional cameras are used to capture a sparsely sampled light field. In both cases, the size of captured data increases with the additional angular information. The recent call for proposal related to compression of light field data by JPEG, also called “JPEG Pleno”, reflects the need of a new and efficient light field compression solution. In this paper, we propose a compression solution for sparsely sampled light field data. In a multi-camera system, each view depicts the scene from a single perspective. We propose to interpret each single view as a frame of pseudo video sequence. In this way, complete MxN views of multi-camera system are treated as M pseudo video sequences, where each pseudo video sequence contains N frames. The central pseudo video sequence is taken as base View and first frame in all the pseudo video sequences is taken as base Picture Order Count (POC). The frame contained in base view and base POC is labeled as base frame. The remaining frames are divided into three predictor levels. Frames placed in each successive level can take prediction from previously encoded frames. However, the frames assigned with last prediction level are not used for prediction of other frames. Moreover, the rate-allocation for each frame is performed by taking into account its predictor level, its frame distance and view wise decoding distance relative to the base frame. The multi-view extension of high efficiency video coding (MV-HEVC) is used to compress the pseudo multi-view sequences. The MV-HEVC compression standard enables the frames to take prediction in both direction (horizontal and vertical d), and MV-HEVC parameters are used to implement the proposed 2D prediction and rate allocation scheme. A subset of four light field images from Stanford dataset are compressed, using the proposed compression scheme on four bitrates in order to cover the low to high bit-rates scenarios. The comparison is made with state-of-art reference encoder HEVC and its real-time implementation X265. The 17x17 grid is converted into a single pseudo sequence of 289 frames by following the order explained in JPEG Pleno call for proposal and given as input to the both reference schemes. The rate distortion analysis shows that the proposed compression scheme outperforms both reference schemes in all tested bitrate scenarios for all test images. The average BD-PSNR gain is 1.36 dB over HEVC and 2.15 dB over X265.

Place, publisher, year, edition, pages
2018.
Keyword [en]
Light field, MV-HEVC, Compression, Plenoptic, Multi-Camera
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-33352OAI: oai:DiVA.org:miun-33352DiVA, id: diva2:1193156
Conference
SPIE Photonics Europe 2018 Strasbourg, France, 22-26 April 2018
Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-04-12Bibliographically approved

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Ahmad, WaqasSjöström, MårtenOlsson, Roger

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Electrical Engineering, Electronic Engineering, Information Engineering
Ahmad, W., Palmieri, L., Koch, R. & Sjöström, M. (2018). The Plenoptic Dataset.

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
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Output format
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