# Exploring Semantic Segmentation on the DCT Representation

arXiv: https://arxiv.org/abs/1907.10015 · HTML: https://mlsanity.com/paper/1907.10015/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [SOLQ: Segmenting Objects by Learning Queries](https://mlsanity.com/paper/2106.02351/) (2021)
- [RGB no more: Minimally-decoded JPEG Vision Transformers](https://mlsanity.com/paper/2211.16421/) (2022)
- [Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain](https://mlsanity.com/paper/2401.13386/) (2024)
- [Deep Learning Based Image Retrieval in the JPEG Compressed Domain](https://mlsanity.com/paper/2107.03648/) (2021)
- [CNNs for JPEGs: A Study in Computational Cost](https://mlsanity.com/paper/2309.11417/) (2023)
- [Learning Single-Image Super-Resolution in the JPEG Compressed Domain](https://mlsanity.com/paper/2512.04284/) (2025)
- [Low-Frequency Shortcuts in Texture-Driven Visual Learning](https://mlsanity.com/paper/2606.03493/) (2026)

## Abstract (arXiv)

Typical convolutional networks are trained and conducted on RGB images. However, images are often compressed for memory savings and efficient transmission in real-world applications. In this paper, we explore methods for performing semantic segmentation on the discrete cosine transform (DCT) representation defined by the JPEG standard. We first rearrange the DCT coefficients to form a preferred input type, then we tailor an existing network to the DCT inputs. The proposed method has an accuracy close to the RGB model at about the same network complexity. Moreover, we investigate the impact of selecting different DCT components on segmentation performance. With a proper selection, one can achieve the same level accuracy using only 36% of the DCT coefficients. We further show the robustness of our method under the quantization errors. To our knowledge, this paper is the first to explore semantic segmentation on the DCT representation.