Energy-constraint rate distortion optimization for compressive sensing-based image coding

Compressive sensing (CS) is an emerging technology which samples a sparse signal at a rate corresponding to its actual information content rather than to its bandwidth. The encoding scheme based on CS features low hardware complexity and power consumption. It is widely used in resource-limited application where the energy consumption determines the lifetime of the system. In this paper, the primary concern is how to configure the encoder to achieve the target energy consumption and target bit rate as well as the optimal picture quality. Firstly, the rate-distortion (R-D) behavior is investigated under the energy constraint. Then a source rate-distortion model and energy consuming model are proposed to quantify the relationship among coding parameters, source encoding rate, distortion, and energy consumption. The accuracy is verified through the experiments. Since the proposed models provide a theoretical basis and a practical guideline for performance optimization, the optimal configuration of coding parameters can be determined. The coding system adjusts its parameter configuration to match the available energy supply of the system while maximizing the picture quality. Simulation results have demonstrated the effectiveness of the proposed approach in achieving the optimal image quality and energy efficiency.