  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 taking the CS measurements and
its index will be transmitted along with the measurements. Based on this index,
the corresponding Bernoulli matrix has been used in CS reconstruction algorithm
to get back the high-frequency wavelet sub-bands at the decoder. At the
decoder, a new Enhanced Approximate Message Passing (EAMP) algorithm has been
proposed to reconstruct the wavelet coefficients and apply the inverse wavelet
transform for restoring back the video frames. Simulation results have
established the superiority of the proposed framework over the existing schemes
and have increased its suitability for VLSI implementation. Moreover, the coded
video is found to be scalable with an increase in a number of levels of wavelet
decomposition.
