VLSI Friendly Framework for Scalable Video Coding based on Compressed Sensing
B.K.N.Srinivasarao, Vinay Chakravarthi Gogineni, Subrahmanyam Mula and, Indrajit Chakrabarti

TL;DR
This paper introduces a VLSI-friendly scalable video coding framework utilizing compressed sensing and 3D wavelet transforms, significantly reducing complexity and improving compression efficiency for hardware implementation.
Contribution
It proposes a novel adaptive measurement scheme and a new reconstruction algorithm, enhancing compression ratio and VLSI suitability over existing methods.
Findings
Uses only 7% of multipliers compared to traditional CS-based coding.
Achieves scalable video quality with increased wavelet decomposition levels.
Demonstrates superior performance through simulation results.
Abstract
This paper presents a new VLSI friendly framework for scalable video coding based on Compressed Sensing (CS). It achieves scalability through 3-Dimensional Discrete Wavelet Transform (3-D DWT) and better compression ratio by exploiting the inherent sparsity of the high-frequency wavelet sub-bands through CS. By using 3-D DWT and a proposed adaptive measurement scheme called AMS at the encoder, one can succeed in improving the compression ratio and reducing the complexity of the decoder. The proposed video codec uses only 7% of the total number of multipliers needed in a conventional CS-based video coding system. A codebook of Bernoulli matrices with different sizes corresponding to the predefined sparsity levels is maintained at both the encoder and the decoder. Based on the calculated l0-norm of the input vector, one of the sixteen possible Bernoulli matrices will be selected for…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Advanced Image Processing Techniques · Image and Signal Denoising Methods
