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STILL IMAGE COMPRESSION WITH ADAPTIVE RESOLUTION VECTOR QUANTIZATION TECHNIQUE

机译:自适应分辨率矢量量化技术的静态图像压缩

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摘要

A novel image compression algorithm based on vector quantization (VQ) technique is proposed in this paper. Adaptive resolution VQ (AR-VQ) method, which is composed of three key techniques, i.e., the edge detection, the resolution conversion, and the block alteration, can realize much superior compression performance than the JPEG and the JPEG-2000. On the compression of the XGA (1024x768 pixels) images including text, for instance, there exist an overwhelming performance difference of 5 to 40 dB in compressed image quality. In addition, we propose a systematic codebook design method of 4x4 and 2x2 pixel blocks for AR-VQ without using learning sequences. According to the method, the codebook can be applied to all kinds of images and exhibits equivalent compression performance to the specific codebooks created individually by conventional learning method using corresponding images.
机译:提出了一种基于矢量量化(VQ)技术的图像压缩算法。自适应分辨率VQ(AR-VQ)方法由边缘检测,分辨率转换和块变更这三项关键技术组成,可以实现比JPEG和JPEG-2000更好的压缩性能。例如,在压缩包含文本的XGA(1024x768像素)图像时,压缩图像质量存在5至40 dB的压倒性性能差异。另外,我们提出了一种不使用学习序列的,用于AR-VQ的4x4和2x2像素块的系统码本设计方法。根据该方法,该码本可以应用于各种图像,并且表现出与通过常规学习方法使用相应图像单独创建的特定码本等效的压缩性能。

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