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Found 9 papers, 1 papers with code
NVTC: Nonlinear Vector Transform Coding
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance.
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression
By quantifying the decoding complexity as a factor in the optimization goal, we are now able to precisely control the RDC trade-off and then demonstrate how the rate-distortion performance of neural image codecs could adapt to various complexity demands.
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Nevertheless, they divide the input image into multiple rectangular regions according to semantics and ignore avoiding information interaction among them, causing waste of bitrate and distorted reconstruction of region boundaries.
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 Weighted Prediction for Efficient Neural Video Compression
Neural video codecs have demonstrated great potential in video transmission and storage applications.
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.
