• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.12
• /
• pp.67-72
• /
• 1993

An adaptive block truncation coding(BTC) using human visual system(HVS) is proposed. To reduce visible blocking effect at sensitive area in HVS. a new category classification coefficient is proposed. The categroy classification coefficient was derived by combining the modified HVS and standard deviation. By computer simulations, we showed that the proposed method reduced blocking effect at low bit rate coding more than the conventional Hui's method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.18 no.10
• /
• pp.1546-1552
• /
• 1993

We proposed an adaptive block truncation coding(BTC) using gradient and a new initial value. We used gradient of sobel operator as a new category classification coefficient to reduce Jagged appearance at edge part. We defined a new initial value to reduce large quantization error in the 4-level quantizer block including edge part. By computer simulations, we showed that the proposed method less computation load, reduced jagged appearance at edge part, also improved PSNR more than the conventional adaptive BTC.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.51-57
• /
• 2010

In this paper, It propose methods compressing BTC image utilizing data hiding technique. BTC is used to compress general digital image into binary image and applied into application such as printer. Additional information, transferred with binary image, is as big as the size of binary image. Therefore, we wish to reduce the total transmission bandwidth by decreasing the additional information with sustaining the small image degradation. Because typical BTC image doesn't have enough space for data hiding, we adopt Adaptive AMBTC (Absolute Moment BTC) algorithm to produce the binary image, and calculate virtual histogram from created binary image and modify this histogram for reducing the additional information. The proposed algorithm can reduce about 6-11 % of the image file size, compared with the existing BTC algorithm, without making perceptible image degradation.