Computationally Efficient Light Field Video Compression Using 5-D Approximate DCT
Five-dimensional (5-D) light field videos (LFVs) capture spatial, angular, and temporal variations in light rays emanating from scenes. This leads to a significantly large amount of data compared to conventional three-dimensional videos, which capture only spatial and temporal variations in light ra...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Journal of Low Power Electronics and Applications |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9268/15/1/2 |
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| Summary: | Five-dimensional (5-D) light field videos (LFVs) capture spatial, angular, and temporal variations in light rays emanating from scenes. This leads to a significantly large amount of data compared to conventional three-dimensional videos, which capture only spatial and temporal variations in light rays. In this paper, we propose an LFV compression technique using low-complexity 5-D approximate discrete cosine transform (ADCT). To further reduce the computational complexity, our algorithm exploits the partial separability of LFV representations. It applies two-dimensional (2-D) ADCT for sub-aperture images of LFV frames with intra-view and inter-view configurations. Furthermore, we apply one-dimensional ADCT to the temporal dimension. We evaluate the performance of the proposed LFV compression technique using several 5-D ADCT algorithms, and the exact 5-D discrete cosine transform (DCT). The experimental results obtained with LFVs confirm that the proposed LFV compression technique provides a more than 250 times reduction in the data size with near-lossless fidelity with a peak-signal-to-noise ratio greater than 40 dB and structural similarity index greater than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9</mn></mrow></semantics></math></inline-formula>. Furthermore, compared to the exact DCT, our algorithms requires approximately 10 times less computational complexity. |
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| ISSN: | 2079-9268 |