A Novel Compression Scheme Based on Hybrid Tucker-Vector Quantization Via Tensor Sketching for Dynamic Light Fields Acquired Through Coded Aperture Camera

Ravishankar, Joshitha; Sharma, Mansi; Khaidem, Sally

doi:10.1109/ic3d53758.2021.9687155

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…In Ref. [43], we show a Hybrid Tucker-TensorSketch Vector Quantisation algorithm that encodes factor matrices in a codebook form through vector quantisation after Tucker decomposition of acquired images from a dynamic light field. The current study details a pipeline of a novel hybrid representation, coding and streaming scheme for static or dynamic light fields.…”

Section: Related Workmentioning

confidence: 99%

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

Ravishankar

Sharma

2022

IET Signal Processing

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This study presents a scheme for efficient representation, coding and streaming of static or dynamic light fields using the authors' novel hybrid Tucker-TensorSketch Karhunen-Loève transform-singular value decomposition via double sketching (HTTS-KLTSVD-DS) algorithm. A deep learning model is employed 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 the authors' 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 the authors' proposed model acts as an optimal transform with good energy compaction property. Encoding of low-rank approximated acquired images using HEVC eliminates intraframe, inter-frame and other intrinsic redundancies in the light field. The authors' complete light field processing pipeline flexibly works for multiple bitrates and is adaptable for a variety of multi-view autostereoscopic platforms. Comparison with stateof-the-art codecs shows reasonable savings and PSNR gains for low and high bitrates, while maintaining good reconstruction quality.

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Section: Related Workmentioning

confidence: 99%

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

Ravishankar

Sharma

2022

IET Signal Processing

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“…In [66], we modeled coded-aperture cameras using convolutional autoencoders and obtained optimal acquired images for processing dynamic light field content. The target was to efficiently encode a light field of a moving scene or a 3D scene changing over time.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, we simulated a coded aperture camera for acquisition of dynamic light field, whereas in the present formulation, the input SAIs are extracted from lenslet images captured by light field cameras. Also, in the proposed scheme, hierarchical scanning patterns of light field subsets are used and redundancies are removed using Tucker-TS algorithm which is different from HTTSVQ presented in [66]. Further, the proposed scheme operates in both the spatial and frequency domain to support a variety of applications.…”

Section: Related Workmentioning

confidence: 99%

“…Further, the proposed scheme operates in both the spatial and frequency domain to support a variety of applications. The two schemes are not directly comparable since we deal with static light fields in the present article, but handle dynamic light field content in [66]. Furthermore, the proposed light field representation scheme has extended functionality to enable general rendering for any given viewpoint, aperture size and depth of focus.…”

Section: Related Workmentioning

confidence: 99%

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A Hierarchical Approach for Lossy Light Field Compression With Multiple Bit Rates Based on Tucker Decomposition via Random Sketching

Ravishankar

Sharma

2022

IEEE Access

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Recently, there has been extensive progress in developing autostereoscopic platforms for display purposes to present real-world 3D scenes. Light fields are the best emerging choice for computational multi-view autostereoscopic displays since they provide an optimized solution to support direction-dependent outputs simultaneously without sacrificing the resolution. We present a novel light field representation, coding and streaming scheme that can efficiently handle large tensor data. Intrinsic redundancies in light field subsets are eliminated through low-rank representation using Tucker decomposition with tensor sketching for various ranks and sketch dimension parameters, making it ideal for streaming and transmission. Apart from removing spatial redundancies, the approximated light field is used to construct a Fourier disparity layers (FDL) representation to further exploit other non-linear, temporal, intra and interview correlations present among the approximated sub-aperture images. Four scanning or view prediction patterns are utilized. The subsets in each pattern hierarchically construct the FDL representation and synthesize subsequent views. Iterative refinement and encoding with HEVC are followed by the final light field reconstruction. The complete end-to-end processing pipeline can flexibly work for multiple bitrates and are adaptable for a variety of multi-view autostereoscopic platforms. The compression performance of the proposed scheme is analyzed on real light fields. We achieved substantial bitrate savings compared to state-of-the-art codecs, while maintaining good reconstruction quality.

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“…Several other strategies were presented as potential means of protecting sensitive information. Another method of data protection is known as data concealing [5]. In order to achieve the goal of information security, data hiding involves embedding large datasets within carriers.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde-Buzo-Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

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A Novel Compression Scheme Based on Hybrid Tucker-Vector Quantization Via Tensor Sketching for Dynamic Light Fields Acquired Through Coded Aperture Camera

Cited by 4 publications

References 25 publications

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

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

A Hierarchical Approach for Lossy Light Field Compression With Multiple Bit Rates Based on Tucker Decomposition via Random Sketching

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

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