使用能量匹配的監(jiān)控視頻自適應(yīng)速率壓縮感知

王健明; 陳建華

doi:10.11999/JEIT190750

使用能量匹配的監(jiān)控視頻自適應(yīng)速率壓縮感知

doi: 10.11999/JEIT190750

王健明,
陳建華^,

云南大學(xué)信息學(xué)院昆明 650504

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61861045)

詳細(xì)信息

作者簡(jiǎn)介:
王健明：男，1984年生，博士生，研究方向?yàn)閿?shù)據(jù)壓縮

陳建華：男，1964年生，教授，博士生導(dǎo)師，研究方向?yàn)樾畔鬏斃碚撆c應(yīng)用

通訊作者:
陳建華　chenjh@ynu.edu.cn

中圖分類號(hào): TN911.73
計(jì)量
- 文章訪問(wèn)數(shù): 835
- HTML全文瀏覽量: 224
- PDF下載量: 99
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-09-29
- 修回日期: 2020-09-27
- 網(wǎng)絡(luò)出版日期: 2020-09-29
- 刊出日期: 2020-12-08

Adaptive-Rate Compressive Sensing Using Energy Matching for Monitoring Video

Jianming WANG,
Jianhua CHEN^,

School of Information Science and Engineering, Yunnan University, Kunming 650504, China

Funds: The National Natural Science Foundation of China (61861045)

摘要

摘要: 獲取信號(hào)稀疏度對(duì)壓縮感知(CS)性能的提升有重大意義，但在采樣端不進(jìn)行完整信號(hào)數(shù)字化采集和存儲(chǔ)的情況下，對(duì)信號(hào)稀疏度進(jìn)行估計(jì)比較困難?，F(xiàn)有方法在稀疏度估計(jì)性能和計(jì)算復(fù)雜度方面難以取得較好的平衡。針對(duì)采樣端對(duì)信號(hào)特性未知的監(jiān)控視頻應(yīng)用，該文提出一種新的使用能量匹配的自適應(yīng)速率壓縮感知方法(ARCS-EM)，通過(guò)觀測(cè)一個(gè)恒定低速率的壓縮感知觀測(cè)結(jié)果來(lái)對(duì)當(dāng)前幀實(shí)際稀疏度進(jìn)行估計(jì)，然后根據(jù)估計(jì)結(jié)果決定當(dāng)前幀應(yīng)執(zhí)行的壓縮感知測(cè)量數(shù)，再進(jìn)行補(bǔ)充測(cè)量得到當(dāng)前幀的優(yōu)化壓縮感知采樣結(jié)果。實(shí)驗(yàn)結(jié)果表明，該方法可以較好地適應(yīng)視頻中前景稀疏度的變化，為每幀圖像分配適當(dāng)?shù)膲嚎s感知測(cè)量速率，在不顯著提高采樣端計(jì)算復(fù)雜度的前提下，有效提高重建視頻的質(zhì)量。
- 圖像信號(hào)處理 /
- 壓縮感知 /
- 自適應(yīng)速率采樣 /
- 能量匹配 /
- 監(jiān)控視頻
Abstract: Signal sparsity is of great significance for the improvement of Compressive Sensing (CS) performance. However, it is difficult to estimate the sparsity when the whole signal is not captured and stored at the sampling side. Few existing mothed can achieve good balance in terms of the sparsity estimation performance and the computational complexity. For the monitoring video applications where the signal characteristics is unknown for sampling devices, a new Adaptive-Rate CS using Energy Matching (ARCS-EM) method is proposed. By observing the measurement results of a low-rate compressive sensing, the actual sparsity of the current frame is estimated and then the rate of measurement for the current frame is determined. Finally, supplementary measurements are performed to obtain the optimized compressive sensing result for the current frame. Experiment results show that the proposed method could allocate suitable measurement rate for each frame to adapt to the variation of sparsity in different frames. The quality of reconstructed videos is effectively improved without noticeably increasing computational complexity in the sampling side.
- Image signal processing /
- Compressive Sensing (CS) /
- Adaptive-rate sampling /
- Energy matching /
- Monitoring video

HTML全文

圖 1 視頻序列范例

下載: 全尺寸圖片幻燈片

圖 2 測(cè)試視頻稀疏程度估計(jì)表現(xiàn)

下載: 全尺寸圖片幻燈片

圖 3 測(cè)試視頻采樣速率

下載: 全尺寸圖片幻燈片

圖 4 測(cè)試視頻圖像重建質(zhì)量

下載: 全尺寸圖片幻燈片

表 1 實(shí)驗(yàn)參數(shù)

參數(shù)	$\varSigma $	$a$	$b$	$\tau $	$r$
視頻序列Hall	2.65	16	128	8	600
視頻序列PETS	2.45	16	128	8	600

下載: 導(dǎo)出CSV

表 2 不同方法的自適壓縮感知平均性能對(duì)比

實(shí)驗(yàn)結(jié)果	Hall視頻平均壓縮感知采樣率	Hall視頻平均峰值信噪比(dB)	PETS視頻平均壓縮感知采樣率	PETS視頻平均峰值信噪比(dB)
Oracle	0.2040	36.59	0.1317	40.02
CDSAM方法	0.2297	36.34	0.2001	39.53
ARCS-CV方法	0.2137	37.03	0.1191	39.07
ARCS-EM方法	0.2232	37.26	0.1350	40.26

下載: 導(dǎo)出CSV

表 3 采樣運(yùn)行時(shí)間對(duì)照表(ms)

運(yùn)行時(shí)間	Hall視頻T₁	Hall視頻T₂	Hall視頻T₃	Hall視頻T	PETS視頻T₁	PETS視頻T₂	PETS視頻T₃	PETS視頻T
CDSAM方法	7.48	104.18	0	111.66	6.76	94.93	0	101.69
ARCS-CV方法	957.26	0.14	2.99×10⁵	3.00×10⁵	526.56	0.11	1.44×10⁵	1.45×10⁵
ARCS-EM方法	800.87	0.42	0	801.29	498.75	0.51	0	499.26

下載: 導(dǎo)出CSV

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