Pyramidal lattice vector quantization for multiscale image coding

Abstract

Introduces a new image coding scheme using lattice vector quantization. The proposed method involves two steps: biorthogonal wavelet transform of the image, and lattice vector quantization of wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, we must truncate and scale the lattice suitably. To meet this goal, we need to know how many lattice points lie within the truncated area. We investigate the case of Laplacian sources where surfaces of equal probability are spheres for the L(1) metric (pyramids) for arbitrary lattices. We give explicit generating functions for the codebook sizes for the most useful lattices like Z(n), D(n), E(s), wedge(16).

Lossy compression of volumetric medical images with 3D dead-zone lattice vector quantization

“…All the images have been compressed by the three methods at rates varying from 0.05 to 1 bit/voxel (bpv). We have used the well-known 9.7 floating-point filter [6] with a four-level 3D DWT or a three-level 3D DWT when the resolution was limited along the z axis (T Z < 64). Furthermore, we have designed a pyramidal codebook whose dead zone and scaling factors (within each subband) have been chosen to optimize the overall rate-distortion trade-off.…”

“…The most approaches combine a threedimensional space decorrelating transform with the extension of a coding algorithm that has proven to be effective on 2D images [3]. For example, Catin et al [4] were the first, to our knowledge, to compress volumetric medical images with Lattice Vector Quantization (LVQ), while many works had been done before on LVQ [5] [6] in the field of 2D real life images.…”

Multistage Lattice Vector Quantization for Hyperspectral Image Compression

“…Lattice vector quantization of wavelet coef cient vectors has been successfully employed for image compression [5,6].…”

Segmentation-based video coding system allowing the manipulation of objects

“…Regarding the low and high-pass filters used for the wavelet transform, bidimensional biorthogonal filters have been chosen. Filter coefficients are given in [1].…”

“…Here, some improvements relying on the use of nonseparable bidimensional wavelet filters, combined with a region-based lattice vector quantization, are presented. The proposed approach is based on the use of the quincunx 2-D wavelet transform, which utilizes 2-D nonseparable low and high-pass filters [1].…”