Vadim Tkhor - Academia.edu (original) (raw)

Vadim Tkhor

Combination of solid academic background in mathematics, communications and practical implementation skills in video codecs, computer graphics, digital image/video/signal processing. Quick and safe implementation of innovative ideas to commercial product. Strong analytical and problem solving skills. Strong programming and software optimization experience. Solid knowledge in processors architecture. Proven ability for innovations and risk-taking. Proven ability to manage a group of developers and scientists, to work either as a team member or independently. Strong skills in planning, presenting, documenting, high quality publications, patenting and completing projects in time. Experience of working in startup condition, under high pressure and distributed teams.

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Papers by Vadim Tkhor

Research paper thumbnail of <title>Optimal losses in image compression: variable quantization based on the multilayer source model</title>

5th International Workshop on Digital Image Processing and Computer Graphics (DIP-94), 1995

Variable quantization (VQ) has become a common lossy operation almost in every modern image compr... more Variable quantization (VQ) has become a common lossy operation almost in every modern image compression method. This is precisely the procedure defining the final decompressed image quality followed by the lossless compression. If the lossless stage of compression is well investigated (Huffman and arithmetic codes), the lossy stage up to the present remains on the level of art basing on the experience of an investigator. A rather general multilayer source model for optimizing (reconciling with the source and human vision properties) variable quantization processes is proposed. This model allows us to analyze the quantization from the new approximation point of view, formalize the optimization task, and propose various simple and effective VQ schemes that can be used as lossy procedure in an arbitrary image compression method.

Research paper thumbnail of Enhancement, Decomposition, And Wavelet-Based Compression Of Space Images

: This report results from a contract tasking Institute for Information Transmission Problems as ... more : This report results from a contract tasking Institute for Information Transmission Problems as follows: The contractor will perform a service consisting of investigation in enhancement, decomposition, and wavelet-based compression of space images. He will research and develop effective nonlinear methods of image enhancement and restoration; construct and demonstrate a wavelet-based image compression algorithm.

Research paper thumbnail of Video signal encoding/decoding method based on adaptive lattice quantization

Research paper thumbnail of Optimal losses in image compression: variable quantization based on the multilayer source model

The variable quantization (VQ) has become a common lossy operation almost in every modern image c... more The variable quantization (VQ) has become a common lossy operation almost in every modern image compression
method. This is precisely the procedure defining the na1 decompressed image quality followed by the lossless compression.
If the lossless stage of compression is well investigated (Huffman and Arithmetic codes), the lossy stage up to the present
remains on the level of art basing on the experience of an investigator. A rather general multilayer source model for
optimizing (reconciling with the source and human vision properties) of variable quaxitization process is proposed. This
model allows to analyze the quantization from the new approximation point of view, formalize the optimization task and propose various simple and effective VQ schemes that can be used as lossy procedure in aibitrary image compression
method.

Research paper thumbnail of <title>Optimal losses in image compression: variable quantization based on the multilayer source model</title>

5th International Workshop on Digital Image Processing and Computer Graphics (DIP-94), 1995

Variable quantization (VQ) has become a common lossy operation almost in every modern image compr... more Variable quantization (VQ) has become a common lossy operation almost in every modern image compression method. This is precisely the procedure defining the final decompressed image quality followed by the lossless compression. If the lossless stage of compression is well investigated (Huffman and arithmetic codes), the lossy stage up to the present remains on the level of art basing on the experience of an investigator. A rather general multilayer source model for optimizing (reconciling with the source and human vision properties) variable quantization processes is proposed. This model allows us to analyze the quantization from the new approximation point of view, formalize the optimization task, and propose various simple and effective VQ schemes that can be used as lossy procedure in an arbitrary image compression method.

Research paper thumbnail of Enhancement, Decomposition, And Wavelet-Based Compression Of Space Images

: This report results from a contract tasking Institute for Information Transmission Problems as ... more : This report results from a contract tasking Institute for Information Transmission Problems as follows: The contractor will perform a service consisting of investigation in enhancement, decomposition, and wavelet-based compression of space images. He will research and develop effective nonlinear methods of image enhancement and restoration; construct and demonstrate a wavelet-based image compression algorithm.

Research paper thumbnail of Video signal encoding/decoding method based on adaptive lattice quantization

Research paper thumbnail of Optimal losses in image compression: variable quantization based on the multilayer source model

The variable quantization (VQ) has become a common lossy operation almost in every modern image c... more The variable quantization (VQ) has become a common lossy operation almost in every modern image compression
method. This is precisely the procedure defining the na1 decompressed image quality followed by the lossless compression.
If the lossless stage of compression is well investigated (Huffman and Arithmetic codes), the lossy stage up to the present
remains on the level of art basing on the experience of an investigator. A rather general multilayer source model for
optimizing (reconciling with the source and human vision properties) of variable quaxitization process is proposed. This
model allows to analyze the quantization from the new approximation point of view, formalize the optimization task and propose various simple and effective VQ schemes that can be used as lossy procedure in aibitrary image compression
method.

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