Light Field Compression With Disparity-Guided Sparse Coding Based on Structural Key Views

Chen, Jie; Hou, Junhui; Chau, Lap-Pui

doi:10.1109/tip.2017.2750413

Cited by 72 publications

(41 citation statements)

References 36 publications

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“…In Liu et al [Liu et al 2016], the grid of LFI is first processed to arrange them in sequential order to get an optimal pseudo-temporal ordering that maximizes when compressed using HEVC encoding. Chen et al [Chen et al 2018] process the LFI using predictive and image-warping methods from which from a small set of key-views are selected and the rest of the LFI are predicted using the key-views. After the pre-processing, the images are temporally ordered using the method in Liu et al [Liu et al 2016] and then compressed using HEVC.…”

Section: Motion-compensated Compression Schemesmentioning

confidence: 99%

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

Pratapa

Manocha

2019

Computer Graphics Forum

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We present a new motion‐compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation schemes and hierarchical compression methods, to exploit redundancies in LFI. The motion compensation schemes capture the redundancies in local regions of the LFI efficiently (local coherence) and the hierarchical schemes capture the redundancies present across the entire LFI (global coherence). Our hybrid approach combines the two schemes effectively capturing both local as well as global coherence to improve the overall compression rate. We compute a tree from LFI using a hierarchical scheme and use phase shifted motion compensation techniques at each level of the hierarchy. Our representation provides random access to the pixel values of the light field, which makes it suitable for interactive rendering applications using a small run‐time memory footprint. Our approach is GPU friendly and allows parallel decoding of LF pixel values. We highlight the performance on the two‐plane parameterized light fields and obtain a compression ratio of 30–800× with a PSNR of 40–45 dB. Overall, we observe a ∼2–5× improvement in compression rates using HMLFC over prior light field compression schemes that provide random access capability. In practice, our algorithm can render new views of resolution 512 × 512 on an NVIDIA GTX‐980 at ∼200 fps.

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Section: Motion-compensated Compression Schemesmentioning

confidence: 99%

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

Pratapa

Manocha

2019

Computer Graphics Forum

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“…Liu et al [2016] order the LF-Images using a pseudo-sequence temporal ordering and compress them using HEVC encoding. Chen et al [2018] use a small set of views to predict the rest of the images using disparity based image-transformations and combine it with a pseudo-sequence method [Liu et al 2016]. 2.2.2 Random Access LFI compression: Levoy and Hanrahan [1996] present a compression technique using vector-quantization (VQ) that provides random access for interactive rendering.…”

Section: Light Field Compressionmentioning

confidence: 99%

RLFC

Pratapa

Manocha

2019

Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

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Figure 1: Our method (RLFC) is based on computing a hierarchy of new images: Representative Key Views (RKV) which capture the redundancies of the LF in the top levels of the hierarchy and Sparse Residual Views (SRV) that capture specific details of the LF in the bottom levels of the hierarchy. We present a visualization of the images (RKV & SRV) computed in RLFC hierarchy on a small sample grid of the LF for two datasets. RLFC achieves compression by removing the insignificant details and exploiting the sparsity of the SRV images in the hierarchy. ABSTRACTWe present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure. The top level (root) of the tree captures the common high-level details across the LFI, and other levels (children) of the tree capture specific low-level details of the LFI. Our decompressing algorithm corresponds to tree traversal operations and gathers the values stored at different levels of the tree. Furthermore, we use bounded integer sequence encoding which provides random access and fast hardware decoding for compressing the blocks of children of the tree. We have evaluated our method for 4D two-plane parameterized light fields. The compression rates vary from 0.08 − 2.5 bits per pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI are 1 − 3 microseconds per channel on an NVIDIA GTX-960 and we can render new views with a resolution of 512 × 512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations. 1

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“…Besides conventional coding methods, also an alternative approach [3] exists that uses deep learning to estimate the 2D view from the sparse sets of 4D views. Another approach [4] proposes own sparse coding scheme for the entire 4D LF based on several optimized key views. The method in [9] decomposes the 4D light field into homography parameters and residual matrix.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of 4D Light Field Compression Methods

Barina¹,

Chlubna²,

Šolony³

et al. 2019

Computer Science Research Notes

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Light field data records the amount of light at multiple points in space, captured e.g. by an array of cameras or by a light-field camera that uses microlenses. Since the storage and transmission requirements for such data are tremendous, compression techniques for light fields are gaining momentum in recent years. Although plenty of efficient compression formats do exist for still and moving images, only a little research on the impact of these methods on light field imagery is performed. In this paper, we evaluate the impact of state-of-the-art image and video compression methods on quality of images rendered from light field data. The methods include recent video compression standards, especially AV1 and XVC finalised in 2018. To fully exploit the potential of common image compression methods on four-dimensional light field imagery, we have extended these methods into three and four dimensions. In this paper, we show that the four-dimensional light field data can be compressed much more than independent still images while maintaining the same visual quality of a perceived picture. We gradually compare the compression performance of all image and video compression methods, and eventually answer the question, "What is the best compression method for light field data?".

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Light Field Compression With Disparity-Guided Sparse Coding Based on Structural Key Views

Cited by 72 publications

References 36 publications

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

RLFC

Evaluation of 4D Light Field Compression Methods

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