Alexander Olegovich Zhirkov

Depth image-based representation and compression for static and animated 3-D objects

This paper describes a new family of three-dimensional (3-D) representations for computer graphics and animation, called depth image-based representations (DIBR), which have been adopted into MPEG-4 Part16: Animation Framework eXtension (AFX). Idea of the approach is to build a compact and photorealistic representation of a 3-D object or scene without using polygonal mesh. Instead, images accompanied by depth values for each pixel are used. This type of representation allows us to build and render novel views of objects and scene with an interactive rate. There are many different methods for the image-based rendering with depths, and the DIBR format is designed to efficiently represent the information necessary for such methods. The main formats of the DIBR family are SimpleTexture (an image together with depth array), PointTexture (an image with multiple pixels along each line of sight), and OctreeImage (octree-like data structure together with a set of images containing viewport parameters). In order to store and transmit the DIBR object, we develop a compression algorithm and bitstream format for OctreeImage representation.

Depth image-based representations for static and animated 3D objects

We describe a novel depth image-based representation (DIBR) that has been adopted into the MPEG-4 animation framework extension (AFX). The idea of this approach is to build a compact representation of a 3D object or scene without storing the geometry information in traditional polygonal form. The main formats of the DIBR family are simple texture (an image together with depth array), point texture (a view of a scene from a single input camera but with multiple pixels along each line of sight), and octree image (octree data structure together with a set of images and their viewport parameters). The designed node specifications and rendering algorithms are addressed. The experimental results show the efficacy and fidelity of the proposed approach.

Texture compression with adaptive block partitions (poster session)

We present image compression method based on block palletizing. Image block is partitioned into four subsets, and each subset is palletized by 2 or 4 colors from the quasioptimal local palette, constructed for the whole block. Index map for the whole block, being the union of index maps for subsets, is thus only 1 or 2 bits deep, while the local palette may consist of 8 or even 16 colors. The local palette has a specific geometrical configuration in RGB color space, determined by only 2 colors. These two colors are stored explicitly, and the rest are reconstructed at the decompression stage. Compressed block consists, essentially, of the index map, palette description and partition description. This format allows fast access to randomly chosen pixels, and high reconstruction quality for compression ratios from 8 to 12, which is useful for texture storage in 3D graphics applications where real-time decompression is crucial.

Graphic Representation Method and Neural Network Recognition of Time-Frequency Vectors of Speech Information

Currently, various time-frequency representations are often used for sound analysis. These representations, on the one hand, are convenient for visible sensation of sound by a human and, on the other hand, can be used for automatically analyzing sound pictures. In this paper, various methods for representation of sound as two-dimensional time-frequency vectors of a fixed dimension and their use for speech and speaker recognition problems are discussed. Probabilistic, distance-based, and neural-network methods for the recognition of these vectors by examples of separate words are considered. Numerical experiments showed that the best among them is the method based on a three-layer neural network, the short-time Fourier transform, and the two-dimensional wavelet transformation. For the speaker recognition problem, a distance-based recognition method employing the adaptive Hermite transform turned out the best among all.