Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

An Evolutionary Multiobjective Approach to Sparse Reconstruction

Abstract: This paper addresses the problem of finding sparse solutions to linear systems. Although this problem involves two competing cost function terms (measurement error and a sparsity-inducing term), previous approaches combine these into a single cost term and solve the problem using conventional numerical optimization methods. In contrast, the main contribution of this paper is to use a multiobjective approach. The paper begins by investigating the sparse reconstruction problem, and presents data to show that knee regions do exist on the Pareto front (PF) for this problem and that optimal solutions can be found in these knee regions. Another contribution of the paper, a new soft-thresholding evolutionary multiobjective algorithm (StEMO), is then presented, which uses a soft-thresholding technique to incorporate two additional heuristics: one with greater chance to increase speed of convergence toward the PF, and another with higher probability to improve the spread of solutions along the PF, enabling an optimal solution to be found in the knee region. Experiments are presented, which show that StEMO significantly outperforms five other well known techniques that are commonly used for sparse reconstruction. Practical applications are also demonstrated to fundamental problems of recovering signals and images from noisy data.

Model-Based Joint Bit Allocation Between Texture Videos and Depth Maps for 3-D Video Coding

Abstract: In 3-D video coding, texture videos and depth maps need to be jointly coded. The distortion of texture videos and depth maps can be propagated to the synthesized virtual views. Besides coding efficiency of texture videos and depth maps, joint bit allocation between texture videos and depth maps is also an important research issue in 3-D video coding. First, we present comprehensive analyses on the impacts of the compression distortion of texture videos and depth maps on the quality of the virtual views, and then derive a concise distortion model for the synthesized virtual views. Based on this model, the joint bit allocation problem is formulated as a constrained optimization problem, and is solved by using the Lagrangian multiplier method. Experimental results demonstrate the high accuracy of the derived distortion model. Meanwhile, the rate-distortion (R-D) performance of the proposed algorithm is close to those of search-based algorithms which can give the best R-D performance, while the complexity of the proposed algorithm is lower than that of search-based algorithms. Moreover, compared with the bit allocation method using fixed texture and depth bits ratio (5:1), a maximum 1.2 dB gain can be achieved by the proposed algorithm.