An adaptive orthogonal transform coding algorithm for images utilizing classification technique
作者： Yoichi Kato , Naoki Mukawa , Sakae Okubo
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摘要: An adaptive orthogonal transform coding algorithm utilizing the classification technique is presented. Coding efficiency for natural images can be improved by adjusting parameters in accordance with local property of images. “Classification” used to categorize small areas an image into classes based on their characteristics. High results from changing adaptively according index. Classification methods using ac energy, binary pattern and vector quantization index are compared, advantage method shown. Also, variable length proposed, its structure characteristics described. A parameter normalization avoid mismatches between input also experiments show excellent performance this algorithm, example, number bits obtaining 40-dB SNR monochrome “GIRL” 0.65 bits/pel, which 20 percent smaller than conventional methods.
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