# High efficiency compression for object detection

arXiv: https://arxiv.org/abs/1710.11151 · HTML: https://mlsanity.com/paper/1710.11151/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

## Built on by (most cited first, 24 of 24)

- [Image Coding for Machines with Omnipotent Feature Learning](https://mlsanity.com/paper/2207.01932/) (2022)
- [A Coding Framework and Benchmark towards Low-Bitrate Video Understanding](https://mlsanity.com/paper/2202.02813/) (2022)
- [Image Coding for Machines with Edge Information Learning Using Segment Anything](https://mlsanity.com/paper/2403.04173/) (2024)
- [SMC++: Masked Learning of Unsupervised Video Semantic Compression](https://mlsanity.com/paper/2406.04765/) (2024)
- [Video Coding for Machines with Feature-Based Rate-Distortion Optimization](https://mlsanity.com/paper/2203.05890/) (2022)
- [AccMPEG: Optimizing Video Encoding for Video Analytics](https://mlsanity.com/paper/2204.12534/) (2022)
- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [Image Coding for Machines with Object Region Learning](https://mlsanity.com/paper/2308.13984/) (2023)
- [Scalable Image Coding for Humans and Machines Using Feature Fusion Network](https://mlsanity.com/paper/2405.09152/) (2024)
- [Saliency-Driven Versatile Video Coding for Neural Object Detection](https://mlsanity.com/paper/2203.05944/) (2022)
- [Accuracy Improvement of Object Detection in VVC Coded Video Using YOLO-v7 Features](https://mlsanity.com/paper/2304.00689/) (2023)
- [Base Layer Efficiency in Scalable Human-Machine Coding](https://mlsanity.com/paper/2307.02430/) (2023)
- [Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression](https://mlsanity.com/paper/2410.07669/) (2024)
- [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (2025)
- [SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences](https://mlsanity.com/paper/2112.14934/) (2021)
- [Recognition-Aware Learned Image Compression](https://mlsanity.com/paper/2202.00198/) (2022)
- [Hyneter: Hybrid Network Transformer for Object Detection](https://mlsanity.com/paper/2302.09365/) (2023)
- [Guided Diffusion for the Extension of Machine Vision to Human Visual Perception](https://mlsanity.com/paper/2503.17907/) (2025)
- [Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective](https://mlsanity.com/paper/2106.08512/) (2021)
- [Video Coding for Machines: Partial transmission of SIFT features](https://mlsanity.com/paper/2201.02689/) (2022)
- [Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image Coding](https://mlsanity.com/paper/2301.08533/) (2023)
- [Image Coding for Machines via Feature-Preserving Rate-Distortion Optimization](https://mlsanity.com/paper/2504.02216/) (2025)
- [CompressAI-Vision: Open-source software to evaluate compression methods for computer vision tasks](https://mlsanity.com/paper/2509.20777/) (2025)
- [Training-Free Adaptive Quantization for Variable Rate Image Coding for Machines](https://mlsanity.com/paper/2511.05836/) (2025)

## Abstract (arXiv)

Image and video compression has traditionally been tailored to human vision. However, modern applications such as visual analytics and surveillance rely on computers seeing and analyzing the images before (or instead of) humans. For these applications, it is important to adjust compression to computer vision. In this paper we present a bit allocation and rate control strategy that is tailored to object detection. Using the initial convolutional layers of a state-of-the-art object detector, we create an importance map that can guide bit allocation to areas that are important for object detection. The proposed method enables bit rate savings of 7% or more compared to default HEVC, at the equivalent object detection rate.