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Performance Improvement of Vector Quantization by Using Threshold

机译:使用阈值的矢量量化性能提高

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Vector quantization (VQ) is an elementary technique for image compression. However, the complexity of searching the nearest codeword in a codebook is time-consuming. In this work, we improve the performance of VQ by adopting the concept of THRESHOLD. Our concept utilizes the positional information to represent the geometric relation within codewords. With the new concept, the lookup procedure only need to calculate Euclidean distance for codewords which are within the threshold, thus sifts candidate codewords easily. Our scheme is simple and suitable for hardware implementation. Moreover, the scheme is a plug-in which can cooperate with existing schemes to further fasten search speed. The effectiveness of the proposed scheme is further demonstrated through experiments. In the experimental results, the proposed scheme can reduce 64% computation with only an extra storage of 512 bytes.
机译:矢量量化(VQ)是用于图像压缩的基本技术。 然而,在码本中搜索最近的码字的复杂性是耗时的。 在这项工作中,我们通过采用阈值的概念来提高VQ的性能。 我们的概念利用位置信息来表示码字中的几何关系。 利用新概念,查找程序只需要计算在阈值内的码字的欧几里德距离,因此容易筛选候选码字。 我们的方案简单,适合硬件实现。 此外,该方案是可以与现有方案配合以进一步紧固搜索速度的插件。 通过实验进一步证明了所提出的方案的有效性。 在实验结果中,所提出的方案可以减少64%的计算,只有512字节的额外存储。

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