This paper considers the problem of lossy neural image compression (NIC). Current state-of-
the-art (SOTA) methods adopt uniform posterior to approximate quantization noise, and …

Quantization is one of the core components in lossy image compression. For neural image
compression, end-to-end optimization requires differentiable approximations of quantization …

Neural image compression has made a great deal of progress. State-of-the-art models are
based on variational autoencoders and are outperforming classical models. Neural …

Image compression aims to reduce the information redundancy in images. Most existing
neural image compression methods rely on side information from hyperprior or context …

End-to-end optimized neural image compression (NIC) has obtained superior lossy
compression performance recently. In this paper, we consider the problem of rate-distortion …

In recent years, neural image compression (NIC) algorithms have shown powerful coding
performance. However, most of them are not adaptive to the image content. Although …

Recent neural image compression (NIC) advances have produced models which are
starting to outperform traditional codecs. While this has led to growing excitement about …

End-to-end trainable models have reached the performance of traditional handcrafted
compression techniques on videos and images. Since the parameters of these models are …

Neural image compression (NIC) has outperformed traditional image codecs in rate-
distortion (RD) performance. However, it usually requires a dedicated encoder-decoder pair …

Neural image compression has been shown to outperform traditional image codecs in terms
of rate-distortion performance. However, quantization introduces errors in the compression …