High Performance Overdrive Using Improved Motion Adaptive Codec ...

IEEE Transactions on Consumer Electronics, Vol. 55, No. 1, FEBRUARY 2009

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High Performance Overdrive Using Improved Motion Adaptive Codec in LCD Jun Wang and Jong-Wha Chong, Member, IEEE Abstract — To improve overdrive performance in LCD, we present an improved motion adaptive codec (IMAC). The IMAC contains two approaches to improve the motion adaptive codec overdrive (MAC-OD). The first is an advanced hybrid image codec (AHIC) for the efficient reduction of the image data stored in frame memory. The second is an advanced motion adaptive selector to reduce the overdrive error. The simulation results show that the IMAC at 6.04:1 compression ratio significantly improves the overdrive performance by 2.268dB in PSNR and achieves better visual quality compared with the conventional MAC-OD at 4:1 compression ratio. The IMAC is implemented with the Verilog HDL and fully synthesizable. It can be applied to shorten liquid crystal response time and minimize motion blur of LCD in TV and desktop applications1. Index Terms —LCD, overdrive, motion adaptive codec, block truncation coding, adaptive quantization coding.

I. INTRODUCTION Strong consumer demand for high performance displays creates the need to shorten liquid crystal response time (LCRT) and minimize motion blur in liquid crystal display (LCD) [1]. A widely used approach to reduce LCRT is overdrive, which is a circuit based on image processing. It also plays an important role in minimizing motion blur due to LCD’s hold-type rendering method [2]. To force the liquid crystal material to react faster, overdrive enlarges the desired change of the pixel value between the current and previous frames [3], [4]. In ideal overdrive, which was developed by Oura and Nakanishi et al. [5] to detect the desired change, a full frame memory is needed to store and output the previous frame. With increasing numbers of pixels displayed on today’s large liquid crystal panels, the amount of image data stored in the frame memory is increasing; therefore, ideal overdrive suffers from the challenge of increasing frame memory size and data transfer rate. It is therefore necessary to compress the image data stored in the frame memory in LCD overdrive. Here we define C-OD as an overdrive using compression to reduce the image data as shown in Fig. 1.

1

This research was sponsored by ETRI SoC Industry Promotion Center. Also, it was supported in part by the Ed-Tech Co. LTD. Jun Wang is with the Department of Electronic and Computer Engineering, Hanyang University, Seoul, 133791 Korea (e-mail: [email protected]). Jong-wha Chong is professor in the Department of Electronic and Computer Engineering, Hanyang University, Seoul, 133791 Korea (e-mail: [email protected]). Manuscript received January 15, 2009

Fig. 1. Block diagram of C-OD in LCD. Note that CF is current frame and PFde is decoded previous frame.

To achieve this, Someya et al. [6-9] first developed a C-OD technique in which an image compression method was applied to reduce the amount of image data and then developed a motion adaptive codec overdrive (MAC-OD) in which motion adaptive processing was introduced in the image decoding section. In these C-ODs, a simple and effective image coding method, block truncation coding (BTC) [10], is used. However, BTC does not perform well when the compression ratio is higher than 3:1. Wubben and Hekstra [11] developed a C-OD based on scalable discrete cosine transform (DCT). However, the computation complexity is much higher in comparison with that of BTC. Sung et al. [12] used characteristics of the human visual system (HVS) to develop a coding method which compresses the converted luminance and chrominance data by different compression methods. Unfortunately, their proposed compression method is not described clearly and only a still image is used for performance evaluation. Chun et al. [13] used a fast discrete wavelet transform to reduce the frame memory. It is a good attempt but only 37.5% of the frame memory is saved. Moreover, 8-line-buffer memory is used; therefore, the total memory cost is not reduced significantly. Han et al. [3] proposed an improved vector quantize based BTC (VQ-BTC) by using sub-block vector. It is the latest research and its improvement is substantial. Although substantial improvements have been made, the COD performance is still insufficient to meet the need for high performance displays in LCD. In our previous research, to improve the C-OD performance, we developed a hybrid image coding (HIC) to compress image data to 1/3 by using the characteristics of the HVS. The HIC represents an RGB color image using YCbCr color space. In addition, we combine our previous proposed adaptive quantization coding (AQC) [14] for the luminance data and the conventional coding method, BTC, for the chrominance data. To further compress the image data to 1/6 and achieve a high performance C-OD, we propose an improved motion adaptive codec (IMAC). The IMAC contains two approaches to improve MAC-OD. The first is an advanced hybrid image codec (AHIC) for the

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J. Wang and J.-W. Chong: High Performance Overdrive Using Improved Motion Adaptive Codec in LCD

efficient reduction of the image data stored in frame memory. The second is an advanced motion adaptive selector (AMAS) to reduce the C-OD error. The remainder of the paper is structured as follows: in Section II, background of C-OD is described. The proposed AHIC algorithm, AHIC architecture, and the AMAS are described in Section III. The simulation results are presented in Section IV. Finally, conclusions are presented in Section V.

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hand, moving pictures should not have many errors in the corrected data. Hence, Jun Someya [8] structured a motion adaptive codec using one encoder, two decoders and a motion adaptive selector as shown in Fig. 2.

II. BACKGROUND OF C-OD A. Requirements of C-OD The cost of the C-OD should be minimized if it is to be applied to the LCD industry. Therefore the focus of C-OD is to minimize the cost of frame memory by compressing the image data stored in the frame memory while maintaining the C-OD performance. The cost of frame memory is related to the amount of data stored in frame memory and the bus width or the data transfer rate between the DRAM and overdrive circuit. In general, the width of a DRAM data bus is 8 bits, 16 bits or 32 bits, and therefore reduction should preferably be conducted so that the data per pixel will be the same as these values. However, a DRAM uses the same data bus for writing and reading, and in practice it would result in a reduction of data into 4 bits/pixel, 8 bits/pixel or 16 bits/pixel for the above bus width [1]. Considering the cost of the overdrive circuit, the lower bus width is better, which leads to the requirement of compressing 24 bits/pixel to 8 bits/pixel or 4 bits/pixel. In C-OD, the image compression ratios should be equal or slightly greater than 3:1 and 6:1 are so-called 1/3 solution and 1/6 solution, respectively. TABLE I DATA TRANSFER RATE OF HDTV 16:9 TV Width Height Mbpf Mbps 1/3Mbpf 1/6Mbpf HD 720p 1280 720 21.1 1265.6 7.0 3.5 WXGA 1368 768 24.0 1442.8 8.0 4.0 HD 1080p 1920 1080 47.5 2847.7 15.8 7.9 Mbpf: mega bits per frame; 1/3Mbpf: 1/3 mega bits per frame; 1/6Mbpf: 1/6 mega bits per frame.

Table I shows data transfer rate of high resolution TV. For example, a 1/3 solution can be applied to 720p or WXGA with 8M DRAM. There are two reasons which drive the requirement of the 1/6 solution. One is to apply the 1/6 solution to 1080p HD using 8M DRAM and the other is to reduce the memory cost further by using 4M DRAM for 720p or WXGA. In conventional C-OD, the 1/6 solution can be achieved by using BTC with block size of 6x8 or 12x4. However, the performance of conventional C-OD using BTC is not satisfactory for high performance display in LCD. A new compression method with high coding performance is needed. B. Principle of motion adaptive codec overdrive (MAC-OD) In C-OD, there are two important points. On the one hand, still pictures should not be wrongly corrected; on the other

Fig. 2. Block diagram of motion adaptive codec overdrive.

This configuration can produce three types of data: the decoded previous frame (PFde) data, the decoded current frame (CFde) data and the current frame (CF) data. PFde is the output of decoder for the data which is read out from the frame memory. CFde is the output of decoder for the data which is directly supplied by the encoder. The reconstructed previous frame (PFre) data can be generated from these three types of image data and it is used as one option of selected previous frame (PFse) data. The motion adaptive selector dynamically selects the PFre data for still pictures and selects PFde data for moving pictures by detecting temporal changes based on pixel in the image data. The relationship can be represented as ⎧ PFre = CF − (CFde − PFde ) if CF = PF PF = ⎨ se ⎩CFde if CF ≠ PF

(1)

Still pictures produce the same image data in two continuous frames, and therefore the errors in the data of these decoded images will have the same values. Since calculating the difference between the data of these two decoded images cancels the errors, the system can properly evaluate the data as a still picture. In moving pictures, the data of each encoded image does not contain the same errors, and therefore errors tend to increase. Consequently, MAC-OD selects the PFde data as the PFse data. The overdrive lookup table gives the outputs as the following OD = LUT (CF , PF ) ideal

(2)

OD = LUT (CF , PF ) mac se

(3)

where ODideal is ideal overdrive output, ODmac is motion adaptive codec overdrive output and LUT means lookup table. III. PROPOSED IMAC This section covers the two approaches in IMAC: proposed AHIC and proposed AMAS.

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A. Algorithm of AHIC In the RGB color space, the three color components are equally important and so are usually all stored at the same resolution. But the human visual system (HVS) is less sensitive to color than to luminance (brightness). So it is possible to represent a color image more efficiently, by separating the luminance from the color information and representing chrominance with a lower resolution than luminance. This reduces the amount of data required to represent the chrominance components without having an obvious effect on visual quality: to the casual observer, there is no apparent difference between an RGB image and an YCbCr image with reduced chrominance resolution [14]. Based on this view we have proposed an algorithm of AHIC. The AHIC converts an RGB image into an YCbCr color space and compresses the luminance data and the down sampled chrominance data by using different methods. Here we use high coding performance method, AQC, for luminance data and conventional method, BTC, for chrominance data. Moreover, to achieve a higher compression ratio, the MIN, which is defined as one of the outputs of AQC, is compressed by another AQC again. We found a model to discover the optimization parameters which are down sampling rate and block size. The AHIC converts RGB color space into YCbCr color space, first. And then it uses a block size of m×n AQC to compress the luminance data and a×1 AQC for the MIN. For the chrominance data, it uses a d:1 down sampling, and applies an a×b BTC. Here, there is a relationship among m, n and b as

m⋅n = b⋅d2

(4)

The block diagram of AHIC encoder is shown in the Fig. 3. Bit rate R can be represented as R=

a(m ⋅ n ⋅ 3 + 5) + (a ⋅ 3 + 13) + 2(16 + a ⋅ b) m⋅n⋅a

(5)

Fig. 3. Block diagram of AHIC encoder.

In a 1/6 solution, the bit rate should be no bigger than 4 bits per pixel and therefore we can get a derivation as following d2 ≥

45 8 + +2 a ⋅b b

(6)

It is easy to get d≥ 3. Here, there are some options which are satisfactory for the condition of this research as: d = 3, a = 8, b = 2; d = 4, a = 8, b = 1. Moreover a square block has better coding performance than a rectangle block, which is proved by the data in [14] and line buffer memory should not be too large, so that the final optimization parameters can be decided as: d = 4, a = 8, b = 1, m = n = 4. That is 4x4 AQC for luminance data, 8x1 AQC for MIN, 4:1 down sampling and 8x1 BTC for chrominance data which is down sampled.

Fig. 4. Data reduction flow of AHIC encoder.

Fig. 4 shows the data reduction flow of AHIC encoder in case of the final optimization parameters. The input data of RGB is 3072 bits, and the data amount is the same as that of the RGB in the stage of YCbCr. The output code 1 (output C1) including step and code data of Y is 424 bits. The output C2 which is the output of an 8x1 AQC for the MIN is 37 bits. The output C3 and the output C4 which are the outputs of an 8x1 BTC for Cb and Cr, respectively, are 24 bits, respectively. Totally, the output bits of the 32x4 block are 509 bits; hence the compression ratio is 6.04:1 which can satisfy the system requirement.

B. Architecture of AHIC Fig. 5 shows the architecture of motion adaptive codec based on the proposed AHIC. It includes an AHIC encoder, a frame memory and two AHIC decoders. The AHIC encoder includes an S/P line buffer, an RGBtoYCbCr module, a 4x4AQC encoder, an 8x1 AQC encoder, two 4:1 down sampling (DS) modules and two 8x1 BTC encoders. The AHIC decoder includes an 8x1 AQC decoder, two 8x1 BTC decoders, a 4x4AQC decoder, two 1:4 up sampling (US) modules, an YCbCrtoRGB module and a P/S line buffer. The S/P line buffer converts serial input data into parallel data, so that the data can be processed by image coding methods based on block. The 4:1 DS reduces the sampling rate of the chrominance data to 1/4. The 1:4 US uses the linear biointerpolation method to enlarge the sampling rates of the decoded chrominance data to the same sampling rates with the luminance data Y. The two AHIC decoders are used for the current frame and previous frame, respectively. The P/S line buffer convert parallel data into serial data so that the next processing, motion adaptive selector, can process the data based on pixel.

J. Wang and J.-W. Chong: High Performance Overdrive Using Improved Motion Adaptive Codec in LCD

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moving pictures and are significant different in case of fast moving pictures. Therefore according to the picture characteristics, the select options can be defined as ⎧CF − (CF − PF ) de de ⎪ ⎪ PF = ⎨CF − (CF − PF ) se de de ⎪ PF ⎪⎩ de

(9)

Fortunately, the select options of still pictures and slow moving pictures are the same. Using a threshold t, the characteristic of picture can be detected as

Fig. 5. Block diagram of motion adaptive codec based on AHIC.

⎧ still picture ⎪ ⎪ ⎨ slow moving picture ⎪ ⎪⎩ fast moving picture Fig. 6. Block diagram of separated down sampling.

if | CF − PF | = 0 de de if | CF − PF | < t de de if | CF − PF | ≥ t de de

(10)

The select options can be simplified as

In hardware implementation, line buffers, which use onchip SRAM, are expensive and affect the system cost. Thus they are critical to the AHIC hardware design. To reduce the size of line buffer, we separate the horizontal and vertical down sampling from the down sampling and then reorder the architecture as shown in Fig. 6. It is evident that using this method has no effect on image quality, because the down sampling operation is separable and commutative. The data to enter into the forward line buffer can be reduced to 50%. The same method is applied to up sampling in AHIC decoder. In this way, the line buffers can be reduced to 50% without any image degradations.

C. Advanced motion adaptive selector (AMAS) In Someya’s previous research, motion adaptive selector was used to switch the reconstructed previous frame data and the decoded previous frame data adapting to still pictures or moving pictures. This way just cancels still pictures being wrongly corrected, but it cannot reduce the C-OD error in the case of moving pictures. Therefore we proposed an advanced motion adaptive selector (AMAS) to solve it. The basic idea of AMAS is to select different option as the selected previous frame data considering the characteristic of still pictures, slow moving pictures and fast moving pictures. Considering the encoder and decoder as a system, the relationship of input (Pinput), output (Poutput) and system error (errorcodec) can be represented as ) P = codec( P output input P −P = error output input codec

= error if error CF PF ≈ error if error CF PF if error ≠ error CF PF

(7) (8)

The system errors for current frame and previous frame are the same in case of still pictures, are similar in case of slow

⎧⎪ CF − (CF − PF ) if | CF − PF | < t de de de de PF = ⎨ se ⎪ PF otherwise ⎩ de

(11)

The AMAS reduce the C-OD error by canceling the system error of codec in case of slow moving pictures which is usually in the majority for moving pictures. The operation based on pixel of selection is very simple and only marginally increases any computing complexity and hardware cost. IV. SIMULATION RESULTS

A. Method of evaluation The peak signal to noise ratio (PSNR), which is generally used to evaluate the performance of image compression methods, is selected to evaluate the C-OD performance. If image compression is used to reduce the image data stored in the frame memory, the reconstructed image will contain compression error. In the configuration shown in Fig. 2, the encoded image data is used only to detect the amount of temporal change between the current and previous frame, and the encoded image data is never displayed directly. Compression error caused by reducing the image data indirectly becomes C-OD error. The C-OD error, which is the difference of the outputs between C-OD and ideal overdrive, leads to the insufficient or excessive correction of response time. Therefore the C-OD performance can be evaluated by investigating the amount of C-OD error. The amount of C-OD error can be indicated by PSNR. B. Evaluation of AHIC To evaluate the proposed algorithm of the AHIC, sequences for simulation are 300 frames of ‘Coastguard’ in CIF [16], 300 frames of ‘Moblecalendar’ in 720p HD [17] and 300

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frames of ‘Station’ in 1080p HD [18]. The sequences are in the format of YCbCr, but the C-OD input is in the format of RGB, and therefore there is a preprocessing of converting YCbCr to RGB. The simulation is carried out in two methods: AHICOD and BTCOD. The AHICOD applies AHIC to MACOD. The AHIC is 32x4 block of AHIC which uses 4x4 AQC for luminance data Y, 8x1 AQC for the MIN, an output from 4x4 AQC and 8x1 BTC for chrominance data. As a reference method for AHICOD, the BTCOD applies BTC to MAC-OD. The BTC uses 4x4 block of BTC to compress the RGB data. We also can select 12x4 block of BTC as a reference method because its compression ratio is similar to 32x4 block of AHIC. But to indirectly compare with latest research result in [3] we select 4x4 block of BTC because the subjective and objective visual quality of the proposed VQ-BTC in [3] at 6:1 compression ratio is similar to or slightly worse than that of basic BTC method at 4:1 compression ratio for 4x4 block of color image. The compression ratios are 6.04:1 and 4:1 for AHIC and BTC, respectively. In both methods the line buffer memory sizes are the same.

45 40

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(a) (b) Fig. 7. Comparison of performances in PSNR for ‘Coastguard’.

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TABLE II AVERAGE RESULTS IN PSNR FOR EACH METHOD Sequence

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AHI COD

45

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25

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Coding performance BTC

AHIC

C-OD performance BTCOD

AHICOD

CIF Coastguard

28.072

35.697

42.141

48.817

720p Moblecalendar

28.105

32.943

42.001

46.529

1080p Station

36.896

39.203

49.984

51.970

720p Mobl ecal endar PSNR ( dB)

PSNR ( dB)

720p Mobl ecal endar

‘Coastguard’, ‘Moblecalendar’ and ‘Station’, respectively, which means that the coding performance of the AHIC is much higher than that of the BTC. Fig. 7(b), Fig. 8(b) and Fig. 9(b) show the simulation results of the AHICOD and the BTCOD in PSNR for ‘Coastguard’, ‘Moblecalendar’ and ‘Station’, respectively, when we apply AHIC and BTC to MAC-OD. The curve of AHICOD is much higher than that of the BTCOD for ‘Coastguard’, ‘Moblecalendar’ and ‘Station’, respectively, which means that the C-OD performance of the AHICOD is much higher than that of the BTCOD. Table II shows the average results in PSNR with the three types of test sequences for the proposed and conventional methods. In case of CIF sequence, ‘Coastguard’, the AHIC achieves 35.697dB and an improvement of 7.606dB as compared with the BTC in PSNR. Referring to C-OD performance, the AHICOD obtains 48.817dB and a progress of 6.655dB as compared with the BTCOD in PSNR. In case of 720p HD sequence, ‘Moblecalendar’, the AHIC achieves 32.943dB and an improvement of 4.838dB as compared with the BTC in PSNR. Referring to C-OD performance, the AHICOD obtains 46.529dB and a progress of 4.528dB as compared with the BTCOD in PSNR. In case of 1080p HD sequence, ‘Station’, the AHIC achieves 39.203dB and an improvement of 2.306dB as compared with the BTC in PSNR. Referring to C-OD performance, the AHICOD obtains 51.970dB and a progress of 1.986dB as compared with the BTCOD in PSNR.

The results are averages of 300 frames in PSNR (dB).

(a) (b) Fig. 9. Comparison of performances in PSNR for ‘Station’.

From the results of the moving sequences of the three resolutions on conditions of the same line-buffer memory and almost the same compression ratio, we observe that the AHIC achieves an improvement of 4.917dB in coding performance as compared with the BTC. Because the proposed VQ-BTC at 6:1 compression ratio in the latest research as in [3] achieves similar performance compared with the BTC at 4:1 compression ratio. We can indirectly confirm that our proposed coding method at 6.04:1 compression ratio offers at least an improvement of 4.917dB in PSNR as compared with the latest research. When the AHIC is applied to the MACOD, the overdrive performance of the AHICOD is also significantly improved by 4.390dB in PSNR as compared with that of the BTCOD.

Fig. 7(a), Fig. 8(a) and Fig. 9(a) show the simulation results of the AHIC and the BTC in PSNR for ‘Coastguard’, ‘Moblecalendar’ and ‘Station’, respectively. The curves of AHIC are much higher than those of the BTC for

C. Evaluation of AMAS To validate the AMAS, AMAS is applied to BTCOD and AHICOD using the CIF sequence ‘Foreman’. The simulations are carried out by changing the threshold from 1 to 256. Fig.

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(b) (a) Fig. 8. Comparison of performances in PSNR for ‘Moblecalendar’.

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J. Wang and J.-W. Chong: High Performance Overdrive Using Improved Motion Adaptive Codec in LCD

10 shows the average C-OD performance with variable threshold. Fig. 10 confirms that the proper threshold which makes the C-OD obtain a best performance exists obviously. 32x4 AHI C

4x4 BTC

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Fig. 10. C-OD performance with variable threshold.

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2.021dB higher in PSNR as compared with that of BTCOD. Fig. 11(c) shows that AMAS obtains 0.456dB improvement in PSNR on average as compared with BTCOD. Fig. 11(d) shows that AMAS obtains 0.247dB improvement in PSNR on average as compared with AHICOD. Fig. 11(e) shows that the AHICAOD achieves an overall improvement of 2.268dB in PSNR on average as compared with that of BTCOD. In Fig. 11(c), BTCAOD performs better over BTCOD in almost all of the frames. However performance of BTCAOD is similar to that of BTCOD in some frames, and some of them are even slightly lower than that of BTCOD. Similar results also occur in Fig. 11(d). The cause might be that the thresholds are not proper in these frames. In the other words, it is difficult to find a uniform proper threshold for all kinds of moving pictures. Therefore the achievement of AMAS is not remarkable when it is applied to AHIC or BTC.

D. Evaluation of IMAC Based on the evaluation result of AMAS, threshold is set to the proper value. The result of IMAC that 32x4 AHIC and AMAS are applied to MAC-OD is defined as AHICAOD. In the same way, the result that 4x4 BTC and AMAS are applied to MAC-OD is defined as BTCAOD. Simulation results of CIF sequence ‘Foreman’ are shown in Fig. 11. 55 AHI C

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Fig. 12. Comparison of the subjective visual quality with ‘Foreman’. (a) (b) Original image. (c) AHIC. (d) BTC.

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(e)

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Fig. 11. Comparison of performances in PSNR with ‘Foreman’.

Fig. 13. Comparison of the overdrive error with ‘Foreman’. (a) AHICAOD. (b) BTCOD. The error is enlarged 4 times and added 128.

Fig. 11(a) shows that the AHIC coding performance of is 2.617dB higher in PSNR as compared with that of BTC. Fig. 11(b) shows that the C-OD performance of AHICOD is

Fig. 12 shows the comparison of subjective visual quality with ‘Foreman’. Fig. 12 (a) and Fig. 12(b) are the original images. Fig. 12(c) is the result image of the proposed AHIC

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and has no obvious block artifacts. Fig. 12(d) is the result image of the conventional method BTC at the 4:1 compression ratio. The subjective visual quality of the proposed AHIC is slightly better than that of BTC. Fig. 13 shows the comparison of the overdrive errors with ‘Foreman’. Fig. 13(a) indicates the overdrive errors of the proposed method AHICAOD. Fig. 13(b) indicates the overdrive errors of the conventional method BTCOD. Fig. 13(b) shows obvious errors in the part of edge, while the proposed method produces some unnoticed errors compared with BTC. Moreover the overdrive errors of the proposed method are not concentrated on the edge, so this method can achieve better subjective visual quality in LCD. The proposed IMAC is implemented with software in C language and with hardware in the Verilog HDL, and thus an FPGA prototype is constructed to validate the hardware operation. Our design is mapped into Xilinx XC3S2000 chip and the size is 4,186 slices except frame memory and internal line buffer memory. The operation speed is about 95MHz. V. CONCLUSIONS

We present an improved motion adaptive codec (IMAC) to improve the overdrive performance in LCD. The IMAC contains two contributions for motion adaptive codec overdrive (MAC-OD). The first is the advanced hybrid image codec (AHIC), which can effectively reduce the image data stored in frame memory. The second is the advanced motion adaptive selector (AMAS), which reduces the C-OD error. Simulation results show that the IMAC at 6.04:1 compression ratio gains an improvement of 2.268dB in PSNR and a better visual quality compared with the conventional MAC-OD using BTC at 4:1 compression ratio or the proposed VQ-BTC in the latest research [3]. By reordering the architecture, the line buffer of AHIC is also reduced to 50% without any image degradation. The IMAC is implemented with the Verilog HDL and fully synthesizable. It can be applied to shorten liquid crystal response time and minimize of motion blur of LCD in TV and desktop applications.

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J. Someya, M. Yamakawa and E. Gofuku, “Reduction of memory capacity in feedforward driving by image compression,” SID Symposium Digest of Technical Papers, vol. 33, no.1, pp. 72-75, May 2002. J. Someya, N. Okuda and H. Tachibana, “A new LCD controller for improvement of response time by compression FFD,” SID Symposium Digest of Technical Papers, vol. 34, no.1, pp. 1346-1349, May 2003. J. Someya, N. Okuda and M. Yamakawa, “The motion adaptive CODEC feedforward driving (macFFD) for HDTV,” SID Symposium Digest of Technical Papers, vol. 34, no.1, pp. 149-151, May 2003. J. Someya and N. Okuda, “A study of motion adaptive CODEC feedforward driving without SDRAM,” SID Symposium Digest of Technical Papers, vol. 35, no.1, pp. 417-419, May 2004. E. Oshri, N. Shelly and H. B. Mitchell, “Interpolative three-level block truncation coding algorithm,” Electronics Letters, vol. 29, no. 14, pp.1267-1268, July 1993. R. H. M. Wubben and G. J. Hekstra, “LCD overdrive frame memory reduction using scalable DCT-based compression,” SID Symposium Digest of Technical Papers, vol. 35, no.1, pp. 1348-1351, May 2004. J. K. Sung, C. G. Kim, J. K. An, M. H. Park and S. D. Yeo, “A new method for improvement of response time by data compression using color space conversion,” SID Symposium Digest of Technical Papers, vol. 36, no.1, pp. 474-477, May 2005. I. J. Chun, H. Mun, J. H. Sung, S. Y. Park and B. G. Kim, “Overdrive frame memory reduction using a fast discrete wavelet transform,” Proc. of 21st Int. Technical Conf. on Circuits/Systems, Computer and Communications (ITC-CSCC’06), Chiang Mai, Thailand, July 2006, pp. 161-164. J. Wang, K. Y. Min and J. W. Chong, “A hybrid image coding in overdrive for motion blur reduction in LCD,” Proc. of 6th Int. Computer Entertainment Computing (ICEC’07), Shanghai, China, Sep. 2007, pp. 263 – 270. I. E. G. Richardson, “H.264 and MPEG-4 Video Compression,” John Wiley & Sons Ltd, UK, pp. 10-16, 2003. Available: Trace. 2008. http://trace.eas.asu.edu/yuv/index.html Available: 2008. www.ldv.ei.tum.de/Members/tobias/sequences/svt Available: 2008. www.ldv.ei.tum.de/Members/tobias/sequences /tmt Jun Wang was born in Suizhou, China, on Feb. 18, 1980. He received a B.S. degree from Wuhan University of Technology, Wuhan, China, in 2001. He is currently pursuing his Ph.D. degree in the Department of Electronic and Computer Engineering, Hanyang University, Seoul, Korean from Sept. 2006. His research interests are image and video processing, the hardware design of real-time H.264 encoder/decoder and MEPG-4.

Jong-Wha Chong (M’ 85) was born in Nonsan, Korea, on March 10, 1950. He received the B.S. and the M.S. degree in Electronics Engineering from Hanyang University, Seoul, Korea, in 1975, and 1979 respectively. He received his Ph.D. degree in Electronics & Communication Engineering from Waseda University, Japan, in 1981. From 1979 to 1980, he was a researcher in C&C research center of Nippon Electronic Company (NEC). From 1983 to 1984, he was a researcher in the Korean Institute of Electronics & Technology (KIET). From 1986 to 1987, he was visiting professor at the University of California, Berkeley, USA. From 1993 to 1994, he was the chairman of CAD & VLSI society in the Institute of the Electronic Engineers of KOREA (IEEK). From 1995 to 1997, he was visiting professor at the University of Newcastle Upon-Tyne, England. From 1997 to 1999, he was the Dean of the Information and Communication Center, Hanyang University. Since 1981, he is a professor of the Department of Electronics Engineering, Hanyang University, Seoul, Korea. Since 2002, he is the Vice chairman of the IEEK. His current research interests are the design of ASIC emulation system, CAD for VLSI, H.264 encoder/decoder design, JPEG2000 encoder design, and communication circuit design, especially UWB modem design.