Datasets › ICB
ICB (Image Compression Benchmark)
A carefully chosen set of high-resolution high-precision natural images suited for compression algorithm evaluation.
The images historically used for compression research (lena, barbra, pepper etc...) have outlived their useful life and its about time they become a part of history only. They are too small, come from data sources too old and are available in only 8-bit precision.
These high-resolution high-precision images have been carefully selected to aid in image compression research and algorithm evaluation. These are photographic images chosen to come from a wide variety of sources and each one picked to stress different aspects of algorithms. Images are available in 8-bit, 16-bit and 16-bit linear variations, RGB and gray.
These Images are available without any prohibitive copyright restrictions.
These images are (c) there respective owners. You are granted full redistribution and publication rights on these images provided:
- The origin of the pictures must not be misrepresented; you must not claim that you took the original pictures. If you use, publish or redistribute them, an acknowledgment would be appreciated but is not required.
- Altered versions must be plainly marked as such, and must not be misinterpreted as being the originals.
- No payment is required for distribution of this material, it must be available freely under the conditions stated here. That is, it is prohibited to sell the material.
- This notice may not be removed or altered from any distribution.
For grayscale evaluation, use the Grayscale 8 bit dataset, for color evaluation, use the Color 8 bit dataset.
@online{icb,
author = {Rawzor},
title = {Image Compression Benchmark},
url = {http://imagecompression.info/}
}
Benchmarks archive 2025-07-28
All 6 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| JPEG Artifact Correction | ICB (Quality 10 Color) | FBCNN PSNR 32.18 | Towards Flexible Blind JPEG Artifacts Removal | jiaxi-jiang/fbcnn +1 | 6 | Compare |
| JPEG Artifact Correction | ICB (Quality 20 Color) | FBCNN PSNR 34.38 | Towards Flexible Blind JPEG Artifacts Removal | jiaxi-jiang/fbcnn +1 | 6 | Compare |
| JPEG Artifact Correction | ICB (Quality 10 Grayscale) | QGAC PSNR 34.73 | Quantization Guided JPEG Artifact Correction | gitlab.com/Queuecumber/quantization-guided-ac | 5 | Compare |
| JPEG Artifact Correction | ICB (Quality 20 Grayscale) | QGAC PSNR 37.12 | Quantization Guided JPEG Artifact Correction | gitlab.com/Queuecumber/quantization-guided-ac | 5 | Compare |
| JPEG Artifact Correction | ICB (Quality 30 Color) | FBCNN PSNR 35.41 | Towards Flexible Blind JPEG Artifacts Removal | jiaxi-jiang/fbcnn +1 | 4 | Compare |
| JPEG Artifact Correction | ICB (Quality 30 Grayscale) | QGAC PSNR 38.43 | Quantization Guided JPEG Artifact Correction | gitlab.com/Queuecumber/quantization-guided-ac | 1 | Compare |
Papers archive 2025-07-28
6 shown of 6 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 7. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Towards Flexible Blind JPEG Artifacts Removal | 2 | 3 | 29 Sep 2021 | ran 9 of 11 samples (2 unverified) |
| Quantization Guided JPEG Artifact Correction | 1 | 6 | 17 Apr 2020 | not harvested |
| Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction | 0 | 4 | 18 Oct 2018 | not harvested |
| DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal | 0 | 4 | 8 Jun 2018 | not harvested |
| Multi-level Wavelet-CNN for Image Restoration | 5 | 5 | 18 May 2018 | not harvested |
| Compression Artifacts Reduction by a Deep Convolutional Network | 4 | 5 | 27 Apr 2015 | ran 0 of 12 samples (12 unverified) |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- ICB
- ICB (Quality 10 Color)
- ICB (Quality 10 Grayscale)
- ICB (Quality 20 Color)
- ICB (Quality 30 Color)
- ICB (Quality 20 Grayscale)
- ICB (Quality 30 Grayscale)
7 variant names, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections