Neural video codecs have recently become competitive with standard codecs such as HEVC
in the low-delay setting. However, most neural codecs are large floating-point networks that …

The state-of-the-art neural video codecs have outperformed the most sophisticated
traditional codecs in terms of rate-distortion (RD) performance in certain cases. However …

This paper provides a survey of the latest developments in visual signal coding and
processing with generative models. Specifically, our focus is on presenting the advancement …

Learned image compression (LIC) has reached a comparable coding gain with traditional
hand-crafted methods such as VVC intra. However, the large network complexity prohibits …

Learning-based image compression has improved to a level where it can outperform
traditional image codecs such as HEVC and VVC in terms of coding performance. In …

The recent trend in neural image compression (NIC) research could be generally grounded
into two categories: analysis-synthesis transform network improvements and entropy …

It has been witnessed that learned image compression has outperformed conventional
image coding techniques and tends to be practical in industrial applications. One of the most …

Deep learning-based image compression has made great progresses recently. However,
some leading schemes use serial context-adaptive entropy model to improve the rate …

Quantizing a floating-point neural network to its fixed-point representation is crucial for
Learned Image Compression (LIC) because it improves decoding consistency for …

Under certain circumstances, advanced neural video codecs can surpass the most complex
traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the …