Sep 15, 2007 - 9 / 30. â Overall transfer process of input video signal to output stimulus. ⢠Example ... Using 42-inch Samsung high-definition PDP(PPM42H3).
Colorimetric Characterization Model for Plasma Display Panel S. Y. Choi, M. R. Luo, P. A. Rhodes, E. G. Heo, and I. S. Choi Journal of Imaging Science and Technology, 51(4), 337-347 ,2007 Presented by In Su Jang
School of Electrical Engineering and Computer Science Kyungpook National Univ.
Abstract
New device characterization model – Applicable to plasma display panels(PDP) – Dissimilar to cathode ray tube and liquid crystal display devices
Intrinsic properties – Large deviation in colorimetric additivity and a variation in color • Due to differences in the number of pixels in a color patch
Three colorimetric characterization models – Define the relationship between the number of sustain pulses and CIEXYZ values • Deriving for full pattern size successfully 2 / 30
• Three-dimensional lookup table(3D-LUT) model
• Single-step polynomial model • Two-step polynomial model including three 1D LUTs with a trasformation matrix
– Selecting single-step polynomial model • Simplicity and good performance • Extended to other pattern sizes
Comprehensive model – Predicting CIEXYZ at sizes different to that used for the training set – One combined training set formed using two different pattern sizes is better than a sing-size training set 3 / 30
1. Introduction
Previous research for PDP characterization – Based on the gain-offset-gamma (GOG) model at one pattern size – Not extended to different pattern size
International Electrotechnical Commission – Methods and parameters for investigating the use of PDPs to display color image – Including the pattern size effect as a display area ratio characteristic – Not considered changes in other characteristics • Color gamut due to pattern size
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Additivity failure comparing to other displays – Need to the development of any device characterization model
Simplified structure of a PDP RGB cell
Fig. 1. The structure of a PDP’s RGB cells. 5 / 30
– Intensity level control • Obtained via the modulation of the number of ac pulse • Average level of input video signal – Increased in proportion to the increase in pattern size – Accompanied by an increase in power consumption
– Automatic power control (APC) function • Regulating power consumption within a certain upper limit • Generating different values dependent on pattern size
This paper investigate about – Relationship • Between the number of sustain pulses of RGB input and resultant CIEXYZ values for a one particular pattern size
– Extending into other pattern sizes 6 / 30
2. Physical properties of a PDP
Overall transfer process of the input video signal – Two methods for controlling the discharge current • Adjusting the number of RGB sustain pulses • Modifying the input level of the video signal
– APC function • Modulating the number of sustain pulses during one frame(16.7ms, 60 frames per second =60Hz) • Frame is divided into eight subfields
Fig. 2. A 16.7ms frame include 8 subfields. The black boxes are the durations of the sustain periods propotional to 1,2,4,…,128.
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– One example of subfield configuration • Number of sustain pulses – Determined by the sum of the product of the sustain pulse limit and the “weight ratios” which correspond to the proportion of subfields turned on – Input value of 5, sustain pulse limit, 2600 (2600ⅹ0.004)+(2600ⅹ0.016)=52 Table 1. One example of subfield configuration and the calculation process for the number of sustain pulses used for a color patch with input value of 5.
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– Overall transfer process of input video signal to output stimulus • Example for full white pattern – RGB(255,255,255) at 1024ⅹ768 – APC=255(max), the number of RGB sustain pulses=466 – Plasma discharge occurs 466times per frame in each RGB cell – CIEXYZ=160.1, 166.1, 185.5
Fig. 3. Flowchart explaining the transformation of input video signal to the emission of light in a PDP. 9 / 30
Pattern size effect – Variety of brightness according to pattern size • Points (1), (2), and (3) correspond to the maximum number of sustain pulses at Fig. 4. Plot of CIE X value versus the number 100%, 60%, and 30% of red sustain pulse at 4 pattern sizes. pattern size, respectively Table 2. Maximum CIE XYZ for RGB and the range of the number of sustain pulses at 4, 30, 60, and 100% pattern size.
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3. Experimental methods
Colorimetric characterization – Using 42-inch Samsung high-definition PDP(PPM42H3) • 1024ⅹ768 with an aspect ration of 16:9 • 512 intensity levels per channel although only 256 were used • Pixel pitch – 0.912mm(H) ⅹ0.693mm(V)
• Total display area – 933.89ⅹ532.22mm2
– Color measurement • CS-1000 tele-spectroradiometer in a dark room – Repeatability with PDP » Evaluated with 15 colors measured twice over a two-month * » The median and maximum ΔEab were 0.38 and 1.18
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– Measurement patches • 100%, 80%, 60%, 45%, 30%, and 4% of the display area • Background was set to black
– Three characterization models • Developed for the 100% pattern size Table 3. (a) Color patches for three characterization models at 100% pattern size.
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• Training set – Empirically determined to have approximately uniform coverage of the CIE XYZ destination color space – 6 digital input values » 1, 15, 43, 66, 105, and 255 » 5-level Æ1, 15, 43, 105, and 255, 4level Æ1, 15, 66, and 255 3-level Æ 1, 43, and 255
Fig. 5. Plot of 216 colors of the 6-level 3D LUT in (a)XY, (b)YZ, and (c)a*b* plane, respectively. 13 / 30
– Two-step polynomial model • Three 1D LUT – Between the normalized RGB luminance values and the number of RGB sustain pulse – 52 equal steps in RGB space
• 6 Transformation matrices – Ideal case where there is little interaction among RGB channels » Primary matrix obtained by measurements of RGB primary colors – Five matrices for nonideal cases » Using polynomial regression between the measured XYZ values for 6-, 5-, 4-, and 3-level 3D LUT and their corresponding normalized RGB luminance values
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– Performance of the three models • Evaluated using three test sets (115 patches) – 4ⅹ4ⅹ4 bright color patches that were chosen to correspond to L* value of 45, 85, 95, and 99 for each of the RGB channels – 24 colors (L* white
Table 5. Tristimulus additivity failure and corresponding color difference for white at 4, 30, 60, and 100% pattern sizes.
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5. Colorimetric characterization model for a PDP
Testing the models’ performance at 100% pattern size – Tested using the 115-test color set – High order polynomial models performed better • Except of using 3- and 4-level training samples – Due to the over-fitting the measurement noise
Table 6. Testing the performance (in terms of ΔEab* ) of the characterization models.
> Reasonable Poor
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– Single model is slightly better than two-step model • Except for the 3ⅹ3 model • Single step polynomial model with a higher order – Already considering the cross-talk between different channels – Needless to include a 1D-LUT normalization
Fig. 11. A comparison of average ΔEab* values against the terms used in the singlestep polynomial model for the test and training set.
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– Testing for forward and reverse characterization models • Testing the numerical reversibility of the single-step model • Using 115-test color set • 3ⅹ11 polynomial model – Acceptable performance
Fig. 12. The process for testing reversibility. Table 7. Reversibility result of polynomials for the 4-level training set in terms of ΔEab* .
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Testing the model’s performance at different pattern sizes – Single-step polynomial models • using 3-, 4-, and 5-level 3D-LUT training data • For each of the 4%, 30%, and 60% pattern sizes
– Very similar performances to 100% pattern size from the 30% Table 8. A comparison of the performances (in terms of ΔEab* ) using the 27-color test set at 30% pattern size.
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Developing a single characterization model – Light output is proportional to • The number of sustain pulses • The range these are regulated by the APC according to pattern size
– New method • To predict the colors displayed at different pattern sizes • Necessary to select an appropriate training set covering the whole range of sustain pulses used for the test set – For example, to predict CIEXYZ values in 80% and 45% size » A set having smaller size than 45% can be used because this can cover a higher range of sustain pulses » Two training set; smaller pattern size than 45% and another having 100% size
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• Second method is to improve model accuracy – Two color patches at different pattern sizes but with same RGB sustain pulses are measured » Subtle difference in their XYZ values could be found – Second training set can be expected to take into account small color difference due to pattern size
• In real applications – Comparing » 4-level 3D LUT for the 30% pattern size » 4-level 3D LUT for the 30% and 100% pattern size – Test data – Three 27-color test sets at 80%, 60%, and 45% size
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• Result – Using 27-color test set at 80%, 60%, and 45% pattern size Table 9. Comparing models’ performance.
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6. Conclusion
Investigating the PDP display – Physical properties – Colorimetric characterization unique • Pattern size influence and substantial additivity failure
Characterization model – Single step polynomial model • 4-level 3D LUT – The number of training set samples is 64 – Mixture of 100% and 30% pattern size
• Successfully applied to estimate colors at different pattern size
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