Still Image Compression with Adaptive Resolution Vector Quantization Technique
Still Image Compression with Adaptive Resolution Vector Quantization Technique
复制标题
采用自适应分辨率矢量量化技术的静态图像压缩
DOI：
10.1080/10798587.2004.10642872
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
T. Ohmi
中科院分区：
文献类型：
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作者：
T. Nakayama;M. Konda;K. Takeuchi;K. Kotani;T. Ohmi
摘要提出了一种基于矢量量化（VQ）技术的图像压缩算法。自适应分辨率矢量量化（AR-VQ）方法由三个关键技术组成，边缘检测、分辨率转换和块变换，可以实现比JPEG和JPEG-2000更优越的上级压缩性能。例如，在包括文本的XGA（1024 × 768像素）图像的压缩上，在压缩图像质量中存在5到40 dB的压倒性性能差异。此外，我们提出了一个系统的码书设计方法的4 × 4和2 × 2像素块的AR-VQ不使用学习序列。根据该方法，码本可以应用于所有类型的图像，并且表现出与使用相应图像通过常规学习方法单独创建的特定码本等效的压缩性能。
Abstract 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.