# Progressive Spatial Recurrent Neural Network for Intra Prediction

arXiv: https://arxiv.org/abs/1807.02232 · HTML: https://mlsanity.com/paper/1807.02232/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://mlsanity.com/paper/2201.09240/) (2022)
- [Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks](https://mlsanity.com/paper/2201.10162/) (2022)
- [End-to-End Learned Image Compression with Quantized Weights and Activations](https://mlsanity.com/paper/2111.09348/) (2021)
- [UAR-NVC: A Unified AutoRegressive Framework for Memory-Efficient Neural Video Compression](https://mlsanity.com/paper/2503.02733/) (2025)
- [Deep Learning-Based Intra Mode Derivation for Versatile Video Coding](https://mlsanity.com/paper/2204.04059/) (2022)
- [Coding for Intelligence from the Perspective of Category](https://mlsanity.com/paper/2407.01017/) (2024)

## Abstract (arXiv)

Intra prediction is an important component of modern video codecs, which is able to efficiently squeeze out the spatial redundancy in video frames. With preceding pixels as the context, traditional intra prediction schemes generate linear predictions based on several predefined directions (i.e. modes) for blocks to be encoded. However, these modes are relatively simple and their predictions may fail when facing blocks with complex textures, which leads to additional bits encoding the residue. In this paper, we design a Progressive Spatial Recurrent Neural Network (PS-RNN) that learns to conduct intra prediction. Specifically, our PS-RNN consists of three spatial recurrent units and progressively generates predictions by passing information along from preceding contents to blocks to be encoded. To make our network generate predictions considering both distortion and bit-rate, we propose to use Sum of Absolute Transformed Difference (SATD) as the loss function to train PS-RNN since SATD is able to measure rate-distortion cost of encoding a residue block. Moreover, our method supports variable-block-size for intra prediction, which is more practical in real coding conditions. The proposed intra prediction scheme achieves on average 2.5% bit-rate reduction on variable-block-size settings under the same reconstruction quality compared with HEVC.