# Fast object detection in compressed JPEG Images

arXiv: https://arxiv.org/abs/1904.08408 · HTML: https://mlsanity.com/paper/1904.08408/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [RGB no more: Minimally-decoded JPEG Vision Transformers](https://mlsanity.com/paper/2211.16421/) (2022)
- [Less is More: Accelerating Faster Neural Networks Straight from JPEG](https://mlsanity.com/paper/2104.00185/) (2021)
- [Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05234/) (2023)
- [All-in-One Image Compression and Restoration](https://mlsanity.com/paper/2502.03649/) (2025)
- [HWRCNet: Handwritten Word Recognition in JPEG Compressed Domain using CNN-BiLSTM Network](https://mlsanity.com/paper/2201.00947/) (2022)
- [Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations](https://mlsanity.com/paper/2506.21171/) (2025)
- [Frequency-domain Learning for Volumetric-based 3D Data Perception](https://mlsanity.com/paper/2302.08595/) (2023)
- [CNNs for JPEGs: A Study in Computational Cost](https://mlsanity.com/paper/2309.11417/) (2023)
- [Intelligent Video Recording Optimization using Activity Detection for Surveillance Systems](https://mlsanity.com/paper/2411.02632/) (2024)
- [Learning Single-Image Super-Resolution in the JPEG Compressed Domain](https://mlsanity.com/paper/2512.04284/) (2025)

## Abstract (arXiv)

Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications. Most of these deep learning models rely on RGB images to localize and identify objects in the image. However in some application scenarii, images are compressed either for storage savings or fast transmission. Therefore a time consuming image decompression step is compulsory in order to apply the aforementioned deep models. To alleviate this drawback, we propose a fast deep architecture for object detection in JPEG images, one of the most widespread compression format. We train a neural network to detect objects based on the blockwise DCT (discrete cosine transform) coefficients issued from the JPEG compression algorithm. We modify the well-known Single Shot multibox Detector (SSD) by replacing its first layers with one convolutional layer dedicated to process the DCT inputs. Experimental evaluations on PASCAL VOC and industrial dataset comprising images of road traffic surveillance show that the model is about 2× faster than regular SSD with promising detection performances. To the best of our knowledge, this paper is the first to address detection in compressed JPEG images.