Search Results for author:
Found 6 papers, 0 papers with code
Image Coding for Machines with Omnipotent Feature Learning
Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success.
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks
Traditional media coding schemes typically encode image/video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream level.
Learning Cross-Scale Prediction for Efficient Neural Video Compression
In this paper, we present the first neural video codec that can compete with the latest coding standard H. 266/VVC in terms of sRGB PSNR on UVG dataset for the low-latency mode.
Versatile Learned Video Compression
We show that the flow prediction module can largely reduce the transmission cost of voxel flows.
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Quantization is one of the core components in lossy image compression.
Causal Contextual Prediction for Learned Image Compression
In this paper, we propose the concept of separate entropy coding to leverage a serial decoding process for causal contextual entropy prediction in the latent space.
