Joint Sampling Rate and Bit-Depth Optimization in Compressive Video Sampling

Abstract

Compressed sensing is a novel technology that exploits sparsity of a signal to perform sampling below the Nyquist rate, and thus has great potential in low-complexity video sampling and compression applications, due to the significant reduction of the sampling rate ( ) and computational complexity. However, most current work about compressive video sampling (CVS) has focused on real-valued measurements without being quantized, and thus is not applicable to engineering practices. Moreover, in many circumstances, the total number of bits is often constrained. Therefore, how to achieve a compromise between the number of measurements and the number of bits per measurement to maximize the visual quality is a great challenge for CVS, which has still not been addressed in literature. In this paper, we first present a novel distortion model that reveals the relationship between distortion, , and quantization bit-depth ( ). Then, using this model, we propose a joint optimization algorithm, by which we are able to easily derive the values of and . Finally, we present an adaptive and unidirectional CVS framework with rate-distortion (RD) optimized rate allocation, wherein we use video characteristics extracted from partial sampling to allocate the required bits for each block, and then implement "optimized" video sampling and measurement quantization with the estimated and , respectively. Simulation results show that our proposal offers comparable RD performance to the conventional method, with a 4.6 dB improvement in the average PSNR.

Bi-level Protected Compressive Sampling

“…Meanwhile, generating a different measurement matrix for every signal could be energy-consuming. Additionally, for engineering practice, using the same measurement matrix for multiple signals or signal segments flavors the subsequent source coding stage of multimedia data sensing, as discussed in [17], [18]. Based on these observations, it is concluded that investigating the behavior of CS-based cipher under the multi-time-sampling (MTS) scenario is both important from the cryptographic and engineering point of view.…”

Model-based quantization for perceptually weighted compressed video sensing

“…Considering the CS quantization, the number of measurements (M ) and the number of bits per measurement (bit-depth (B)) represent the main tradeoffs in the rate-distortion (RD) performance [3]. An empirical distortion model for compressed video sensing (CVS) is proposed in [4], along with a rateallocation module for both the sampling rate (SR) and the quantization bit-depth for each block in video frames. However, in [4], the distortion model parameters are sequence dependent.…”

“…An empirical distortion model for compressed video sensing (CVS) is proposed in [4], along with a rateallocation module for both the sampling rate (SR) and the quantization bit-depth for each block in video frames. However, in [4], the distortion model parameters are sequence dependent.…”

“…However, in [5] and [6], quantization of CS measurements are not taken into consideration. Unlike previous works in [4,5,6], this letter proposes generalized RD-optimized SR empirical models for perceptual CVS to enable static SR and bit-depth. We develop the quantized versions of the perceptual CVS frameworks in [5].…”

A scheme for distributed compressed video sensing based on hypothesis set optimization techniques

“…It also obtains the information of local complex structure in natural images, and fully exploits the redundancy between frames by combining the block matching technique commonly used in video. Because of these advantages mentioned above, the application of BCS in DCVS has been researched considerably (Do et al 2009;Prades-Nebot et al 2009;Fowler et al 2012;Liu et al 2013Liu et al , 2014. Among them, the BCS-based multi-hypothesis (MH-BCS) scheme has gained wide spread attention due to its high recovery quality.…”