# BlockCNN: A Deep Network for Artifact Removal and Image Compression

arXiv: https://arxiv.org/abs/1805.11091 · HTML: https://mlsanity.com/paper/1805.11091/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 4 of 4)

- [Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior](https://mlsanity.com/paper/2504.11286/) (2025)
- [Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN](https://mlsanity.com/paper/2311.04263/) (2023)
- [NU-Class Net: A Novel Approach for Video Quality Enhancement](https://mlsanity.com/paper/2401.01163/) (2024)
- [Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation](https://mlsanity.com/paper/2602.20097/) (2026)

## Abstract (arXiv)

We present a general technique that performs both artifact removal and image compression. For artifact removal, we input a JPEG image and try to remove its compression artifacts. For compression, we input an image and process its 8 by 8 blocks in a sequence. For each block, we first try to predict its intensities based on previous blocks; then, we store a residual with respect to the input image. Our technique reuses JPEG's legacy compression and decompression routines. Both our artifact removal and our image compression techniques use the same deep network, but with different training weights. Our technique is simple and fast and it significantly improves the performance of artifact removal and image compression.