The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Conditional Probability Models for Deep Image Compression
Open accessAbstract
Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder. The main idea is to directly model the entropy of the latent representation by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto-encoder. During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation. Our experiments show that this approach, when measured in MS-SSIM, yields a state-of-the-art image compression system based on a simple convolutional auto-encoder.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · cited 12×
“MS-SSIM reportedly correlates better with human perception of distortion than mean squared error (MSE).”
MethodInfluentialSingle image super-resolution from transformed self-exemplars
Huang, J.-B., Singh, A., & Ahuja, N. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299156 · Cited by 3,472
Conditional Image Generation with PixelCNN Decoders
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05328 · Cited by 1,833
Pixel Recurrent Neural Networks
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.06759 · Cited by 1,437
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
Timofte, R., De Smet, V., & Van Gool, L. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16817-3_8 · Cited by 1,408
Self-citationEnd-to-end Optimized Image Compression
Ballé, J., Laparra, V., & Simoncelli, E. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01704 · Cited by 1,275
Cited by
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules
Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00796 · Cited by 1,161
Chen, T., Liu, H., Ma, Z., Shen, Q., Cao, X., & Wang, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3058615 · Cited by 346
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Blau, Y., & Michaeli, T. (2019) · arXiv · doi:10.48550/arxiv.1901.07821 · Cited by 198
Deep Learning-Based Video Coding: A Review and a Case Study
Liu, D., Li, Y., Lin, J., Li, H., & Wu, F. (2021) · ACM Computing Surveys · doi:10.1145/3368405 · Cited by 192
Energy compaction-based image compression using convolutional autoencoder
Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2938345 · Cited by 97
Metadata
- Authors
- Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., & Gool, L. V.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4394-4402
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2782637256
- Cited by
- 490
- References
- 19
- FWCI
- 17.92
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Conditional Probability Models for Deep Image Compression. https://app.cochat.ai/papers/10.1109/cvpr.2018.00462
- The paper
- Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., & Gool, L. V. (2018). Conditional Probability Models for Deep Image Compression. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00462
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
