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Hybrid Image Compression by Using Vector Quantization (VQ) and Vector-Embedded Karhunen-Loève Transform (VEKLT)

机译:使用矢量量化(VQ)和矢量嵌入式Karhunen-Loève变换(VEKLT)的混合图像压缩

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In this paper, a new block-transform-based image compression scheme is proposed by combining vector quantization (VQ) and two transformations, discrete cosine transform (DCT) and vector-embedded Karhunen-Loève transform (VEKLT). First, 8×8 blocks from an input image are normalized and vector-quantized. Then, the difference between the original block and its vector-quantized block is transformed by VEKLT. In parallel, the original block is transformed by DCT. All blocks are classified into two categories (DCT and VEKLT) to minimize arithmetic code length. After that, quad tree decomposition is performed on the binary index image which indicates where a block belongs to one of the two categories. Experimental results show that the proposed scheme outperforms JPEG in peak signal-to-noise ratio (PSNR) and visual quality at high detail.
机译:本文提出了一种新的基于块变换的图像压缩方案,该方案将矢量量化(VQ)与两个变换(离散余弦变换(DCT)和矢量嵌入的Karhunen-Loève变换(VEKLT))相结合。首先,对来自输入图像的8×8块进行归一化和矢量量化。然后,通过VEKLT转换原始块及其矢量量化块之间的差异。并行地,原始块由DCT转换。所有块都分为两类(DCT和VEKLT),以最大程度地减少算术代码的长度。之后,对二进制索引图像执行四叉树分解,该二进制树索引图像指示块属于两个类别之一的位置。实验结果表明,该方案在峰值细节信噪比(PSNR)和视觉质量上均优于JPEG。

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