Deep image compression systems mainly contain four components: encoder, quantizer,
entropy model, and decoder. To optimize these four components, a joint rate-distortion …

Image compression is a widely used technique to reduce the spatial redundancy in images.
Recently, learning based image compression has achieved significant progress by using the …

In deep image compression, uniform quantization is applied to latent representations
obtained by using an auto-encoder architecture for reducing bits and entropy coding …

Recent deep image compression methods have achieved prominent progress by using
nonlinear modeling and powerful representation capabilities of neural networks. However …

In recent deep image compression neural networks, the entropy model plays a critical role in
estimating the prior distribution of deep image encodings. Existing methods combine …

Deep learning based image compression methods have achieved superior performance
compared with transform based conventional codec. With end-to-end Rate-Distortion …

One critical component in lossy deep image compression is the entropy model, which
predicts the probability distribution of the quantized latent representation in the encoding …

In this work, we propose a new deep image compression framework called Complexity and
Bitrate Adaptive Network (CBANet) that aims to learn one single network to support variable …

Transform and entropy models are the two core components in deep image compression
neural networks. Most existing learning-based image compression methods utilize …

In this paper, we propose a novel variable-rate learned image compression framework with
a conditional autoencoder. Previous learning-based image compression methods mostly …