Directionally classified subspace image vector quantization

L. Po, Chok-Ki Chan
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引用次数: 3

Abstract

Describes a new image coding scheme called directionally classified subspace vector quantization which is based on the dimensionality reduced subspace distortion measurement technique and the classified vector quantization technique for reducing the computational complexity and memory requirement of the image vector quantizer. In the new coding scheme, the 4*4 image block is classified into one of nine classes according to the directional content of the image block which is vector quantized using appropriate Hadamard transform subspace distortion measure. The classification is based on the horizontal and vertical gradients of the image block. The two gradient parameters form a 2-dimensional space which can be partitioned into 9 regions and each region correspond to a class of vectors. As the subspace vector quantization is applied on the restricted class of vector, extremely low dimensionality subspace distortion measures can be used. Thus, the computational complexity and memory requirement of the coder are both significantly reduced, while the reconstructed image quality is preserved for edges.<>
方向分类子空间图像矢量量化
为了降低图像矢量量化器的计算复杂度和内存需求,提出了一种基于降维子空间失真测量技术和分类矢量量化技术的方向分类子空间矢量量化图像编码新方案。在新的编码方案中,根据图像块的方向内容将4*4图像块分为9类之一,并使用适当的Hadamard变换子空间失真度量对图像块进行矢量量化。分类是基于图像块的水平和垂直梯度。这两个梯度参数构成了一个二维空间,该空间可划分为9个区域,每个区域对应一类向量。由于子空间矢量量化应用于受限的矢量类,因此可以使用极低维的子空间畸变措施。因此,编码器的计算复杂度和内存需求都大大降低,同时保留了重建图像的边缘质量。
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