Sensitivity analysis of multilayer perceptrons applied to focal-plane image compression

2007 ◽  
Vol 1 (1) ◽  
pp. 79 ◽  
Author(s):  
J.G.R.C. Gomes ◽  
A. Petraglia ◽  
S.K. Mitra
Keyword(s):  
Sensitivity Analysis ◽  
Image Compression ◽  
Focal Plane ◽  
Multilayer Perceptrons ◽  
Plane Image

CCD focal-plane image reorganization processors for lossless image compression

1992 ◽  
Vol 27 (3) ◽  
pp. 398-405 ◽  
Author(s):  
S.E. Kemeny ◽  
H.H. Torbey ◽  
H.E. Meadows ◽  
R.A. Bredthauer ◽  
M.A. La Shell ◽  
...  
Keyword(s):  
Image Compression ◽  
Focal Plane ◽  
Lossless Image Compression ◽  
Plane Image

A circuit for focal-plane image compression using vector quantization

2011 ◽  
Author(s):  
Fernanda D. V. R. Oliveira ◽  
Hugo L. Haas ◽  
Jose Gabriel R. C. Gomes ◽  
Antonio Petraglia
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Focal Plane ◽  
Plane Image

Smart pixels in a focal-plane image compression system

2002 ◽  
Vol 34 (6) ◽  
pp. 429-437
Author(s):  
Hiroyuki Arima ◽  
Masahiko Mori ◽  
Toyohiko Yatagai
Keyword(s):  
Image Compression ◽  
Focal Plane ◽  
Compression System ◽  
Plane Image ◽  
Smart Pixels

scholarly journals CMOS Image Sensor Featuring Current-Mode Focal-Plane Image Compression

2013 ◽  
Vol 8 (1) ◽  
pp. 14-21
Author(s):  
Fernanda D. V. R. Oliveira ◽  
Hugo L. Haas ◽  
José Gabriel R. C. Gomes ◽  
Antonio Petraglia
Keyword(s):  
Image Compression ◽  
Focal Plane ◽  
High Speed ◽  
Low Voltage ◽  
Current Mode ◽  
Image Sensor ◽  
Cmos Process ◽  
Target Pattern ◽  
Differential Pulse Code Modulation ◽  
Plane Image

The interest in focal-plane processing techniques, by which image processing is carried out at the pixel level, has increased since the advent of active pixel sensors in the middle 90’s. By sharing processing circuitry by a group of neighboring pixels such techniques enable high-speed imaging operation and massive parallel computation. Focal-plane image compression is particularly interesting, because it allows for further reduction in data rates. The proposed approach also benefits from processing currents rather than voltages, which not only suits current-mode APS imagers, but also enables the circuits to operate at low voltage supply levels and achieve high speed. Moreover, arithmetic computations such as additions and scaling are easily implemented in current mode. Whereas current-mode imaging architectures produce higher fixed pattern noise (FPN) figures than their voltage-mode counterparts, low FPN can be achieved by applying correlated double sampling (CDS) and gain correction techniques. This work presents a 32 × 32 gray-level imaging integrated circuit featuring focal plane image compression, such that for each 4 × 4 pixel block, analog circuits implement differential pulse-code modulation, linear transform, and vector quantization. Other processing functions implemented in the chip are CDS and A/D conversion. Theoretical details are described, as well as the test setup of the chip fabricated in a 0.35 μm CMOS process. To validate the proposed technique, experimental results and captured photographs are shown. The CMOS imager compresses captured images at 0.94 bits/pixel for an overall power consumption below 40 mW (white image), which is equivalent to approximately 36 μW per pixel. Using photographs taken from bar-target pattern inputs, it is shown that details up to 2 cycles/c mare preserved in the decoded images.

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