Light field compression with disparity-guided sparse coding based on structural key views
Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and t...
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sg-ntu-dr.10356-1423072020-06-18T09:19:36Z Light field compression with disparity-guided sparse coding based on structural key views Chen, Jie Hou, Junhui Chau, Lap-Pui School of Electrical and Electronic Engineering Engineering::Computer science and engineering Light Field Structural Key View Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by an LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose an LF codec with disparity guided Sparse Coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV). The sparse coding is based on a limited number of optimally selected SKVs; yet the entire LF can be recovered from the coding coefficients. By keeping the approximation identical between encoder and decoder, only the residuals of the non-key views, disparity map, and the SKVs need to be compressed into the bit stream. An optimized SKV selection method is proposed such that most LF spatial information can be preserved. To achieve optimum dictionary efficiency, the LF is divided into several coding regions, over which the reconstruction works individually. Experiments and comparisons have been carried out over benchmark LF data set, which show that the proposed SC-SKV codec produces convincing compression results in terms of both rate-distortion performance and visual quality compared with Joint Exploration Model: with 37.9% BD-rate reduction and 1.17-dB BD-PSNR improvement achieved on average, especially with up to 6-dB improvement for low bit rate scenarios. 2020-06-18T09:19:35Z 2020-06-18T09:19:35Z 2017 Journal Article Chen, J., Hou, J., & Chau, L.-P. (2018). Light field compression with disparity-guided sparse coding based on structural key views. IEEE Transactions on Image Processing, 27(1), 314-324. doi:10.1109/TIP.2017.2750413 1057-7149 https://hdl.handle.net/10356/142307 10.1109/TIP.2017.2750413 28910766 2-s2.0-85038209985 1 27 314 324 en IEEE Transactions on Image Processing © 2017 IEEE. All rights reserved. |
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Engineering::Computer science and engineering Light Field Structural Key View Chen, Jie Hou, Junhui Chau, Lap-Pui Light field compression with disparity-guided sparse coding based on structural key views |
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Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by an LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose an LF codec with disparity guided Sparse Coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV). The sparse coding is based on a limited number of optimally selected SKVs; yet the entire LF can be recovered from the coding coefficients. By keeping the approximation identical between encoder and decoder, only the residuals of the non-key views, disparity map, and the SKVs need to be compressed into the bit stream. An optimized SKV selection method is proposed such that most LF spatial information can be preserved. To achieve optimum dictionary efficiency, the LF is divided into several coding regions, over which the reconstruction works individually. Experiments and comparisons have been carried out over benchmark LF data set, which show that the proposed SC-SKV codec produces convincing compression results in terms of both rate-distortion performance and visual quality compared with Joint Exploration Model: with 37.9% BD-rate reduction and 1.17-dB BD-PSNR improvement achieved on average, especially with up to 6-dB improvement for low bit rate scenarios. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Chen, Jie Hou, Junhui Chau, Lap-Pui |
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Article |
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Chen, Jie Hou, Junhui Chau, Lap-Pui |
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Chen, Jie |
title |
Light field compression with disparity-guided sparse coding based on structural key views |
title_short |
Light field compression with disparity-guided sparse coding based on structural key views |
title_full |
Light field compression with disparity-guided sparse coding based on structural key views |
title_fullStr |
Light field compression with disparity-guided sparse coding based on structural key views |
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Light field compression with disparity-guided sparse coding based on structural key views |
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light field compression with disparity-guided sparse coding based on structural key views |
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2020 |
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https://hdl.handle.net/10356/142307 |
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