Improvement of Decision on Coding Unit Split Mode and Intra-Picture Prediction by Machine Learning

High efficiency Video Coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The reference software (i.e., HM) have included the implementations of the guidelines in appliance with the new standard. The software includes both encoder and decoder functionality. Machine learning (ML) works with data and processes it to discover patterns that can be later used to analyze new trends. ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. In this research project, in compliance with H.265 standard, we are focused on improvement of the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. We used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in our convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in HM software, and it was proved to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results

Machine learning-based coding decision making in H.265/HEVC CTU division and intra prediction

Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. High efficiency video coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. In this research project, in compliance with H.265 standard, the authors focused on improving the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. The authors used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in the convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in the reference software for HEVC, and it was proven to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results

A Homogeneous Gallium(III) Compound Selectively Catalyzes the Epoxidation of Alkenes

We demonstrate that a simple gallium­(III) complex, [Ga­(phen)₂Cl₂]Cl (phen = 1,10-phenanthroline), can serve as a homogeneous catalyst for the epoxidation of alkenes. The olefin epoxidations proceed relatively quickly at mild temperatures and, under optimum conditions, are highly selective for the epoxide product

Catalysis of Alkene Epoxidation by a Series of Gallium(III) Complexes with Neutral N‑Donor Ligands

Six gallium­(III) complexes with N-donor ligands were synthesized to study the mechanism of Ga^III-catalyzed olefin epoxidation. These include 2:1 ligand/metal complexes with the bidentate ligands ethylenediamine, 5-nitro-1,10-phenanthroline, and 5-amino-1,10-phenanthroline, as well as 1:1 ligand/metal complexes with the tetradentate <i>N</i>,<i>N</i>′-bis­(2-pyridylmethyl)-1,2-ethanediamine, the potentially pentadentate <i>N</i>,<i>N</i>,<i>N</i>′-tris­(2-pyridylmethyl)-1,2-ethanediamine, and the potentially hexadentate <i>N</i>,<i>N</i>,<i>N</i>′,<i>N</i>′-tetrakis­(2-pyridylmethyl)-1,2-ethanediamine. In solution, each of the three pyridylamine ligands appears to coordinate to the Ga^III through four donor atoms. The six complexes were tested for their ability to catalyze the epoxidation of alkenes by peracetic acid. Although the complexes with relatively electron-poor phenanthroline derivatives display faster initial reactivity, the gallium­(III) complexes with the polydentate pyridylamine ligands appear to be more robust, with less noticeable decreases in their catalytic activity over time. The more highly chelating trispicen and tpen are associated with markedly decreased activity

A Homogeneous Gallium(III) Compound Selectively Catalyzes the Epoxidation of Alkenes

We demonstrate that a simple gallium­(III) complex, [Ga­(phen)₂Cl₂]Cl (phen = 1,10-phenanthroline), can serve as a homogeneous catalyst for the epoxidation of alkenes. The olefin epoxidations proceed relatively quickly at mild temperatures and, under optimum conditions, are highly selective for the epoxide product

Catalysis of Alkene Epoxidation by a Series of Gallium(III) Complexes with Neutral N‑Donor Ligands

Six gallium­(III) complexes with N-donor ligands were synthesized to study the mechanism of Ga^III-catalyzed olefin epoxidation. These include 2:1 ligand/metal complexes with the bidentate ligands ethylenediamine, 5-nitro-1,10-phenanthroline, and 5-amino-1,10-phenanthroline, as well as 1:1 ligand/metal complexes with the tetradentate <i>N</i>,<i>N</i>′-bis­(2-pyridylmethyl)-1,2-ethanediamine, the potentially pentadentate <i>N</i>,<i>N</i>,<i>N</i>′-tris­(2-pyridylmethyl)-1,2-ethanediamine, and the potentially hexadentate <i>N</i>,<i>N</i>,<i>N</i>′,<i>N</i>′-tetrakis­(2-pyridylmethyl)-1,2-ethanediamine. In solution, each of the three pyridylamine ligands appears to coordinate to the Ga^III through four donor atoms. The six complexes were tested for their ability to catalyze the epoxidation of alkenes by peracetic acid. Although the complexes with relatively electron-poor phenanthroline derivatives display faster initial reactivity, the gallium­(III) complexes with the polydentate pyridylamine ligands appear to be more robust, with less noticeable decreases in their catalytic activity over time. The more highly chelating trispicen and tpen are associated with markedly decreased activity