Researching | Efficient multi-bands image compression method for remote cameras

Journals >Chinese Optics Letters >Volume 15 >Issue 2 >Page 022801 > Article

Chinese Optics Letters
Vol. 15, Issue 2, 022801 (2017)

Efficient multi-bands image compression method for remote cameras

Jin Li^1、2、3, Fengdeng Liu^1、2、3, and Zilong Liu^{1、2、3、4、*}

Author Affiliations

¹Department of precision instrument, Tsinghua University, Beijing 100084, China

²State Key Laboratory of Precision Measurement Technology and Instruments, Beijing 100084, China

³Collaborative Innovation Center for Micro/Nano Fabrication, Device and System, Beijing 100084, China

⁴Optic Division, National Institute of Metrology, Beijing 100029, China

show less

DOI: 10.3788/COL201715.022801 Cite this Article Set citation alerts

Jin Li, Fengdeng Liu, Zilong Liu. Efficient multi-bands image compression method for remote cameras[J]. Chinese Optics Letters, 2017, 15(2): 022801 Copy Citation Text

show less

Abstract

In this Letter, we propose an efficient compression algorithm for multi-spectral images having a few bands. First, we propose a low-complexity removing spectral redundancy approach to improve compression performance. Then, a bit plane encoding approach is applied to each band to complete the compression. Finally, the experiments are performed on multi-spectral images. The experiment results show that the proposed compression algorithm has good compressive property. Compared with traditional approaches, the proposed method can decrease the average peak signal noise ratio by 0.36 dB at 0.5 bpp. The processing speed reaches 23.81 MPixels/s at the working frequency of 88 MHz, which is higher than the traditional methods. The proposed method satisfies the project application.

ρ (A, B) = \frac{\sum_{i = 1}^{m} [(a_{i} - E [a]) (b_{i} - E [b])]}{\sqrt{\sum_{i = 1}^{m} {(a_{i} - E [a])}^{2} \sum_{i = 1}^{m} {(b_{i} - E [b])}^{2}}},

(1)

View in Article

ρ_{X, Y} = \frac{Cov (X, Y)}{SD (X) SD (Y)} = \frac{E [(X - E (X)) (Y - E (Y))]}{\sqrt{E ({(X - E (X))}^{2})} \sqrt{E ({(Y - E (Y))}^{2})}} .

(2)

View in Article

G = \frac{P}{2} + \frac{P}{4} + \dots + \frac{P}{2^{L}} .

(3)

View in Article

H_{i} = {[\begin{matrix} h_{1, 1, i} & h_{2,1, i} & h_{3, 1, i} & \dots & h_{L, 1, i} \\ h_{1, 2, i} & h_{2, 2, i} & h_{3, 2, i} & \dots & h_{L, 2, i} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ h_{1, M, i} & h_{2, M, i} & h_{3, M, i} & \dots & h_{L, M, i} \end{matrix}]}_{L \times M}, i = 1, 2,

(4)

View in Article

H = [\begin{matrix} H_{1}^{'} \\ H_{2}^{'} \end{matrix}] = [\begin{matrix} \begin{matrix} h_{1, 1, 1} & h_{2, 1, 1} & h_{3, 1, 1} & \begin{matrix} \dots & h_{L, M, 1} \end{matrix} \end{matrix} \\ \begin{matrix} h_{1, 2, 2} & h_{2, 2, 2} & h_{3, 2, 2} & \begin{matrix} \dots & h_{L, M, 2} \end{matrix} \end{matrix} \end{matrix}],

(5)

View in Article

B_m = [\begin{matrix} {mean}_{1}, & {mean}_{2} \end{matrix}],

(6)

View in Article

H = [\begin{matrix} \begin{matrix} h_{1, 1, 1} - {mean}_{1} & h_{2, 1, 1} - {mean}_{1} & h_{3, 1, 1} - {mean}_{1} & \begin{matrix} \dots & h_{L, M, 1} - {mean}_{1} \end{matrix} \end{matrix} \\ \begin{matrix} h_{1, 2, 2} - {mean}_{2} & h_{2, 2, 2} - {mean}_{2} & h_{3, 2, 2} - {mean}_{2} & \begin{matrix} \dots & h_{L, M, 2} - {mean}_{2} \end{matrix} \end{matrix} \end{matrix}] .

(7)

View in Article

Cov (H) = \frac{1}{4} H^{T} H = [\begin{matrix} {cov}_{11} & {cov}_{12} \\ {cov}_{21} & {cov}_{22} \end{matrix}] .

(8)

View in Article

V = [\begin{matrix} v_{11} & v_{12} \\ v_{21} & v_{22} \end{matrix}] .

(9)

View in Article

v_{11} = v_{22} = \sqrt{\frac{1}{2} + \frac{({cov}_{11} - {cov}_{22})}{2 η}} = \sqrt{1 - v_{21}^{2}},

(10)

View in Article

v_{21} = - v_{12} = \frac{{cov}_{12}}{| {cov}_{12} |} \sqrt{\frac{1}{2} - \frac{{cov}_{11} - {cov}_{22}}{2 η}},

(11)

View in Article

η = \sqrt{{({cov}_{11} - {cov}_{22})}^{2} + 4 {cov}_{12} {cov}_{21}} .

(12)

View in Article

λ = [\begin{matrix} λ_{1} & 0 \\ 0 & λ_{2} \end{matrix}] .

(13)

View in Article

λ_{1} = \frac{{cov}_{11} + {cov}_{22} + η}{2} λ_{2} = \frac{{cov}_{11} + {cov}_{22} - η}{2},

(14)

View in Article

Cov (H) V = V λ, Cov (H) = V λ V^{- 1}, V^{T} Cov (H) V = V^{T} V λ V^{- 1} V = V^{T} V λ .

(15)

View in Article

V^{T} V = [\begin{matrix} v_{11}^{2} + v_{22}^{2} & v_{11} v_{21} + v_{12} v_{22} \\ v_{11} v_{21} + v_{12} v_{22} & v_{21}^{2} + v_{22}^{2} \end{matrix}] .

(16)

View in Article

V^{T} V = [\begin{matrix} v_{11}^{2} + v_{22}^{2} & 0 \\ 0 & v_{21}^{2} + v_{22}^{2} \end{matrix}] .

(17)

View in Article

V^{T} Cov (H) V = [\begin{matrix} λ_{1} (v_{11}^{2} + v_{22}^{2}) & 0 \\ 0 & λ_{2} (v_{21}^{2} + v_{22}^{2}) \end{matrix}] = Λ,

(18)

View in Article

V^{T} Cov (H) V = \frac{1}{4} V^{T} H H^{T} V .

(19)

View in Article

V^{T} Cov (H) V = \frac{1}{4} V^{T} H {(V^{T} H)}^{T} = \frac{1}{4} G G^{T} = Cov (G) .

(20)

View in Article

G = V^{T} H .

(21)

View in Article

Figures&Tables (11)

References (16)

Copy Citation Text

Jin Li, Fengdeng Liu, Zilong Liu. Efficient multi-bands image compression method for remote cameras[J]. Chinese Optics Letters, 2017, 15(2): 022801

Download Citation

Set citation alerts for the article

Set citation alerts for the article

Save the article for my favorites

Paper Information

Recommended Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology

Set citation alerts for the article

Please enter your email address