In this paper, we propose an adaptive BTC algorithm using the characteristics of the error signals. The BTC algorithm has a avantage that it is low computational complexity, but a disadvantage that it produces the ragged edges in the reconstructed images for th esloping regions beause of coding the input with 2-level signals. Firstly, proposed methods classify the input into low, medium, and high activity blocks based on the variance of th einput. Using 1-level quantizer for low activity block, 2-level for medium, and 4-level for high, it is adaptive methods that reduce bit rates and the inherent quantization noises in the 2-level quantizer. Also, in case of processing high activity block, we propose a new quantization level allocation algorithm using the characteristics of the error signals between the original signals and the reconstructed signals used by 2-level quantizer, in oder that reduce bit rates superior to the conventional 4-level quantizer. Especially, considering the characteristics of input block, we reduce the bit rates without incurrng the visual noises.
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