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STILL IMAGE COMPRESSION WITH MEAN-RESIDUAL DOMAIN ADAPTIVE RESOLUTION VECTOR QUANTIZATION TECHNIQUE

机译:均值域自适应矢量量化技术的静态图像压缩

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摘要

With the glowing importance of an advanced image processing methods for digital image, an efficient compression algorithm is increasingly important. In this paper we present progressive image compression algorithm based on Vector Quantization (VQ). Mean-Residual domain Adaptive Resolution Vector Quantization technique (MAR-VQ) that combines the adaptive resolution method with the mean-residual method has been developed. The adaptive resolution method changes the resolution of image adaptively according to the form of a pixel block texture in order to increase the performance of compound image compression. In addition, the mean-residual method was introduced as one of the additional techniques to increase continuous-tone image quality by VQ. As a result, MAR-VQ can realize much superior compression performance than the worldwide standard JPEG2000.
机译:随着高级图像处理方法对数字图像的日益重要,有效的压缩算法变得越来越重要。在本文中,我们提出了基于矢量量化(VQ)的渐进式图像压缩算法。已经开发了将自适应分辨率方法与均值残差方法相结合的均值残差域自适应分辨率矢量量化技术(MAR-VQ)。自适应分辨率方法根据像素块纹理的形式自适应地更改图像的分辨率,以提高复合图像压缩的性能。此外,均值残差法是通过VQ提高连续色调图像质量的其他技术之一。结果,MAR-VQ可以实现比全球标准JPEG2000更高的压缩性能。

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