Authors: Wei Jiang Junjie Yang
Publish Date: 2016/01/14
Volume: 86, Issue: 1, Pages: 85-97
Abstract
Compressive Sensing CS is an emerging technology which can encode a signal into a small number of incoherent linear measurements and reconstruct the entire signal from relatively few measurements Different from former coding scheme which distortion mainly comes from quantizer distortion and bit rate are related to quantization and compressive sampling in the compressive sensing based image coding schemes Moreover the total number of bits is often constrained in the practical application Therefore under the given bit rate how to balance the number of measurements and quantization step size to minimization the distortion is a great challenge In this paper a fast Lagrange multiplier solving method is proposed for the compressive sensing based image coding scheme Then using the solved Lagrange multiplier the optimal number of measurements and quantization step size are determined based on the ratedistortion criteria Experimental results show that the proposed algorithm improves objective and subjective performances substantiallyThis work is supported by National Natural Science Foundation of China NSFC 61401269 61371125 61202369 Natural Science Foundation of Shanghai 14ZR1417400 Shanghai Technology Innovation Project 10110502200 11510500900 Innovation Program of Shanghai Municipal Education Commission 12ZZ17613YZ105 Project of Science and Technology Commission of Shanghai Municipality 10PJ1404500 Leading Academic Discipline Project of Shanghai Municipal Education Commission J51303
Keywords: