# Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images

arXiv: https://arxiv.org/abs/2006.16581 · HTML: https://mlsanity.com/paper/2006.16581/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 28 of 28)

- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs](https://mlsanity.com/paper/2305.11860/) (2023)
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- [Path Independent Equilibrium Models Can Better Exploit Test-Time Computation](https://mlsanity.com/paper/2211.09961/) (2022)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results](https://mlsanity.com/paper/2204.09314/) (2022)
- [Multi-Exit Semantic Segmentation Networks](https://mlsanity.com/paper/2106.03527/) (2021)
- [Hybrid Agents for Image Restoration](https://mlsanity.com/paper/2503.10120/) (2025)
- [Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images](https://mlsanity.com/paper/2103.06116/) (2021)
- [Conditional computation in neural networks: principles and research trends](https://mlsanity.com/paper/2403.07965/) (2024)
- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Progressive Training of A Two-Stage Framework for Video Restoration](https://mlsanity.com/paper/2204.09924/) (2022)
- [T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit](https://mlsanity.com/paper/2207.06613/) (2022)
- [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://mlsanity.com/paper/2501.07451/) (2025)
- [Tiny Models are the Computational Saver for Large Models](https://mlsanity.com/paper/2403.17726/) (2024)
- [FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits](https://mlsanity.com/paper/2304.10306/) (2023)
- [NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks](https://mlsanity.com/paper/2311.00428/) (2023)
- [On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2403.17154/) (2024)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus](https://mlsanity.com/paper/2211.10984/) (2022)
- [Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting](https://mlsanity.com/paper/2405.12705/) (2024)
- [Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain](https://mlsanity.com/paper/2402.17200/) (2024)
- [DistilDoc: Knowledge Distillation for Visually-Rich Document Applications](https://mlsanity.com/paper/2406.08226/) (2024)
- [Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations](https://mlsanity.com/paper/2506.21171/) (2025)
- [Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation](https://mlsanity.com/paper/2307.16555/) (2023)
- [Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation](https://mlsanity.com/paper/2312.07180/) (2023)
- [Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference](https://mlsanity.com/paper/2511.16137/) (2025)
- [Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis](https://mlsanity.com/paper/2607.16319/) (2026)

## Abstract (arXiv)

Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming. Besides, it is common in practice that compressed images are with unknown quality and it is intractable for existing approaches to select a suitable model for blind quality enhancement. In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Specifically, our approach blindly and progressively enhances the quality of compressed images through a dynamic deep neural network (DNN), in which an early-exit strategy is embedded. Then, our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images. Consequently, slight artifacts can be removed in a simpler and faster process, while the severe artifacts can be further removed in a more elaborate process. Extensive experiments demonstrate that our RBQE approach achieves state-of-the-art performance in terms of both blind quality enhancement and resource efficiency. The code is available at https://github.com/RyanXingQL/RBQE.