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Цифровая сеть

Publications

We provide scientific validation of our results. In collaboration with other researchers, we've published a set of papers. Here are the main ones.   

Discrete Atomic Compression of JPEG Images:
A Comparative Study with a Progressive DCT-Based Coder and Evaluation Across a Diverse Set of Established and
State-of-the-Art CNN Classifiers

This paper shows that a DAC algorithm can significantly reduce the memory footprint of digital photos without degrading the performance of subsequent CNN classification.

Image Representation by Atomic Functions: Features for Classification

The paper demonstrates that image representation by atomic functions leads to a reduction in the computational complexity of large-scale image classification.

Digital Image Representation by Atomic Functions: Features for Computer Vision and Machine Learning

The paper focuses on the use of image representation by atomic functions in machine learning (ML) and computer vision (CV).

Lossy Compression of Remote Sensing Images by Atomic Functions: DAC Improvements

This book chapter is an extended version of our paper on spatial complexity reduction of the DAC algorithm.

On the Impact of Discrete Atomic Compression on Image Classification by Convolutional Neural Networks

This paper explores the negative impact of lossy compression by the DAC algorithm on performance of CNNs.

Complexity Reduction in DAT-Based Image Processing

This paper proposes an approach to the complexity reduction in image compression based on Discrete Atomic Transform (DAT).

Coding Improvement in the DAC Algorithm

The paper concentrates on increasing the performance of Discrete Atomic Compression (DAC). The proposed approach ensures a higher compression ratio and, hence, greater memory savings.

Image Compression and Protection Systems Based on Atomic Functions

This paper introduces the concept of atomic functions based image processing systems (AFIPS). It generalizes our previous results and outline further research & development.

Comparison of DAT with DCT in a Viewpoint of Current Image Processing and Analysis Trends

Discrete Atomic Transform is a core of the created image processing methods. Is it better than other tools, in particular Discrete Cosine Transform? The paper answers this question.  

Spatial Complexity Reduction in Remote Sensing Image Compression by Atomic Functions

Can memory expenses be reduced without performance degradation, even when processing a complicated data? Yes, and the paper shows it.

Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems

The paper presents a new concept of the developed method use in edge processing of digital images.

Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control

The paper describes quality control mechanism that ensures the desired results in terms of lossy image compression.

On the Applications of the Special Class of Atomic Functions: Practical Aspects and Perspectives

The paper concentrates on the complexity of the atomic functions based methods. It explains why these algorithms are so fast.   

Discrete Atomic Compression with Different Structures of Discrete Atomic Transform: Efficiency Comparison and Perspectives of Application to Digital Images Privacy Protection

Can images be protected using atomic functions? Yes. The paper proposes digital image encryption approach.

Discrete Atomic Compression of Digital Images

This is our first paper on the applying atomic functions to image compression. 

Approximation of periodic functions using mups(x)

Atomic functions are perfect constructive tools. This is a very ambitious statement, and the paper provides its fundamental proof.    

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