# Reducing Complexity of HEVC: A Deep Learning Approach

arXiv: https://arxiv.org/abs/1710.01218 · HTML: https://mlsanity.com/paper/1710.01218/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 8 of 8)

- [Diffusion-aided Extreme Video Compression with Lightweight Semantics Guidance](https://mlsanity.com/paper/2602.05201/) (2026)
- [Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation](https://mlsanity.com/paper/2205.03587/) (2022)
- [Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression](https://mlsanity.com/paper/2110.01805/) (2021)
- [Partition Map-Based Fast Block Partitioning for VVC Inter Coding](https://mlsanity.com/paper/2504.18398/) (2025)
- [Synthesizing multi-layer perceptron network with ant lion, biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings](https://mlsanity.com/paper/2102.08928/) (2021)
- [BLINC: Lightweight Bimodal Learning for Low-Complexity VVC Intra Coding](https://mlsanity.com/paper/2201.07823/) (2022)
- [Blurry Video Compression: A Trade-off between Visual Enhancement and Data Compression](https://mlsanity.com/paper/2311.04430/) (2023)
- [Accelerating HEVC Intra Partitioning via a CNN-Hierarchical Attention Transformer Hybrid](https://mlsanity.com/paper/2605.29063/) (2026)

## Abstract (arXiv)

High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the proceeding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the bruteforce search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network. First, we establish a large-scale database including substantial CU partition data for HEVC intra- and inter-modes. This enables deep learning on the CU partition. Second, we represent the CU partition of an entire coding tree unit (CTU) in the form of a hierarchical CU partition map (HCPM). Then, we propose an early-terminated hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the encoding complexity of intra-mode HEVC can be drastically reduced by replacing the brute-force search with ETH-CNN to decide the CU partition. Third, an early-terminated hierarchical LSTM (ETH-LSTM) is proposed to learn the temporal correlation of the CU partition. Then, we combine ETH-LSTM and ETH-CNN to predict the CU partition for reducing the HEVC complexity for inter-mode. Finally, experimental results show that our approach outperforms other state-of-the-art approaches in reducing the HEVC complexity at both intra- and inter-modes.