# Advanced ANN Architecture for the CTU Partitioning in All Intra HEVC

## TL;DR

This paper explores using advanced neural networks to speed up video encoding in HEVC, focusing on All Intra mode.

## Contribution

The novelty lies in evaluating ResNet- and DenseNet-based architectures for CTU partitioning in HEVC encoding.

## Key findings

- ResNet- and DenseNet-based models effectively reduce encoding time for CTU partitioning.
- Compression efficiency and network size trade-offs are analyzed for different architectures.
- The study highlights limitations for hardware-constrained devices and application-specific considerations.

## Abstract

Due to the growing complexity of video encoders, the optimization of the parameters of the encoding process is becoming an important issue. In recent years, this has become an important field of application of neural networks. Artificial neural networks in video encoders are used to accelerate the video encoder operation. This paper demonstrates the use of different ResNet- and DenseNet-type architectures to accelerate the CTU partitioning algorithm in HEVC in All Intra mode. The paper demonstrates the results of an exhaustive evaluation of different proposed architectures, considering compression efficiency, network size, and encoding time reduction. Multiple pros and cons of the proposed architectures are presented in the Conclusions, considering various limitations that may be important for a given application, like hardware-constrained sensor networks or standalone small devices operating with images and videos.