About
Image Compression is an application of data compression for digital images to lower their storage and/or transmission requirements.
Source: Variable Rate Deep Image Compression With a Conditional Autoencoder
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
Subtasks
Datasets
Latest papers without code
An Implementation of Vector Quantization using the Genetic Algorithm Approach
The application of machine learning(ML) and genetic programming(GP) to the image compression domain has produced promising results in many cases.
Discrete Cosine Transform in JPEG Compression
Via an intensive literature study, this paper first introduces DCT and JPEG Compression.
Progressive Neural Image Compression with Nested Quantization and Latent Ordering
We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream.
Overfitting for Fun and Profit: Instance-Adaptive Data Compression
At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents.
Improved Autoregressive Modeling with Distribution Smoothing
While autoregressive models excel at image compression, their sample quality is often lacking.
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Recently deep learning-based image compression has shown the potential to outperform traditional codecs.
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4, 000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets.
Learned Block-based Hybrid Image Compression
Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications.
Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding
More specifically, we designed a compressor network with Refine-Net for coding residual signals and motion vectors.
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs
Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures.