基于多参考帧假设优化的压缩感知重构算法

doi:10.11959/j.issn.1000-436x.2017297

Abstract

Abstract:

In multi-hypothesis based distributed compressed video sensing systems,the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However,the acquiring of the hypothesis set has not been concerned in existing works.A reconstruction algorithm based on multi-reference frames hypothesis optimization (MRHO) was proposed.This algorithm expanded the selection of hypothesis vectors by increasing the number of reference frames.The quality of the prediction set was improved by hypotheses optimization selection under the same size with the original hypothesis set.Simulation results show that the proposed MRHO algorithm effectively improves the reconstructed quality of the distributed compressed video sensing scheme.

Key words: compressed sensing, distributed compressive video sensing, multi-hypothesis set optimization, multi-reference frames selection

CLC Number:

TP919.8

Yong-hong KUO,Ru-quan WANG,Jian CHEN. Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing[J]. Journal on Communications, 2017, 38(12): 1-9.

Figures/Tables 13

References 19

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采样率	平均重构时间/s		平均重构质量/dB
采样率	未进行优化	进行优化后	未进行优化	进行优化后
0.1	211.059 2	153.522 9	33.151 7	33.656 3
0.2	215.876 8	157.015 6	35.973 9	36.504 7
0.3	217.261 3	167.191 3	37.905 2	38.309 5
0.4	216.886 8	156.303 1	39.365 6	39.861 5
0.5	219.221 9	160.226 5	40.995 1	41.446 1

Key帧序号	MH-BCS-SPL的Key帧重构PSNR/dB	文献[15]的Key帧二次重构PSNR/dB	本文的Key帧二次重构PSNR/dB
1	42.183 9	42.136 5	43.252 1
9	40.882 5	40.943 8	42.833 9
17	40.937 3	41.085 7	43.059 2
25	40.998 6	41.129 3	43.256 1
33	41.293 3	41.156 2	43.416 9
41	41.028 4	40.959 5	43.114 3
49	41.180 7	41.074 8	43.273 7
57	41.410 9	41.171 1	43.572 4
65	40.629 9	40.456 5	42.050 9
73	42.112 4	41.743 6	43.658 6
81	40.936 1	40.867 8	42.088 8
平均值	41.235 8	41.156 8	43.052 4

采样率	MC-BCS-SPL/s	MH-BCS-SPL/s	MRHO-DCVS/s
0.1	29.485 5	58.970 9	157.761 8
0.2	29.869 7	59.739 4	158.061 0
0.3	29.648 3	59.296 6	161.004 0
0.4	20.275 4	60.550 7	156.303 0
0.5	30.126 4	60.252 8	160.226 5

采样率	MC-BCS-SPL/s	MH-BCS-SPL/s	MRHO-DCVS/s
0.1	39.353 6	78.707 1	161.470 0
0.2	40.397 0	79.898 5	167.274 1
0.3	39.949 3	80.794 0	165.027 2
0.4	40.270 7	80.541 3	163.676 4
0.5	40.364 4	80.728 7	166.375 8

采样率	MC-BCS-SPL/s	MH-BCS-SPL/s	MRHO-DCVS/s
0.1	29.333 0	58.665 9	164.654 3
0.2	29.211 7	58.542 5	168.372 1
0.3	29.271 3	58.423 4	165.489 7
0.4	30.666 5	61.333 0	165.002 8
0.5	30.359 3	60.718 6	166.721 4

Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing

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