An Integrated Learning and Approximation Scheme for Coding of Static or Dynamic Light fields Based on Hybrid Tucker-KLTSVD via Tensor Double-Sketching
Joshitha R, Mansi Sharma
IET Signal Processing, 2022
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Abstract
This article presents a scheme for efficient representation, coding and streaming of static or dynamic light fields using our novel hybrid Tucker-TensorSketch Karhunen–Loeve Transform-Singular Value Decomposition via double sketching (HTTS-KLTSVD-DS) algorithm. We employ a deep learning model to obtain acquired images from the light fields by simulating coded aperture patterns. These acquired images can represent the entire light field and are low-rank approximated using HTTS-KLTSVD-DS. Incorporation of double sketching using TensorSketch allows our algorithm to work faster in a single pass itself and there is no need to store large Kronecker products of Tucker decomposition in the memory. This provides an efficient transmission and streaming adaptability of the light field, making it suitable for 3D display applications. Besides, compact representation of factor matrices by KLT-SVD in our proposed model acts as an optimal transform with good energy compaction property. Encoding of low-rank approximated acquired images using HEVC eliminates intra-frame, inter-frame and other intrinsic redundancies in the light field. Our complete end-to-end light field processing pipeline flexibly works for multiple bitrates and is adaptable for a variety of multi-view autostereoscopic platforms. Comparison with state-of-the-art codecs shows reasonable savings and PSNR gains for low and high bitrates, while maintaining good reconstruction quality.
IETSignalProcessing2022.pdfCitation
Joshitha Ravishankar,Mansi Sharma, "An integrated learning and approximation scheme for coding of static or dynamic light fields based on hybrid Tucker–Karhunen–Loève transform-singular value decomposition via tensor double sketching," 29 June 2022 https://doi.org/10.1049/sil2.12141
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