Search Results for author:
Found 12 papers, 3 papers with code
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Then we only encode non-zero channels in the encoding and decoding process, which can greatly reduce the encoding and decoding time.
ROI-based Deep Image Compression with Swin Transformers
Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks.
Multi-rate adaptive transform coding for video compression
Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression.
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance.
MuZero with Self-competition for Rate Control in VP9 Video Compression
Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services.
A Quantitative Approach To The Temporal Dependency in Video Coding
Its predictive coding scheme propagates the quantization distortion through the prediction chain and creates a temporal dependency.
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image.
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Recently deep learning-based image compression has shown the potential to outperform traditional codecs.
Neural Rate Control for Video Encoding using Imitation Learning
In modern video encoders, rate control is a critical component and has been heavily engineered.
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
Learned image compression has recently shown the potential to outperform the standard codecs.
Learned Variable-Rate Image Compression with Residual Divisive Normalization
Recently it has been shown that deep learning-based image compression has shown the potential to outperform traditional codecs.
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
A compact representation of the input image is also generated and encoded as the first enhancement layer.
