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Found 9 papers, 4 papers with code
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed.
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
Currently, there is a high demand for neural network-based image compression codecs.
Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model
In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data.
Deep probabilistic model for lossless scalable point cloud attribute compression
In recent years, several point cloud geometry compression methods that utilize advanced deep learning techniques have been proposed, but there are limited works on attribute compression, especially lossless compression.
Learning-Based Conditional Image Coder Using Color Separation
Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction.
Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors
Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds.
Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation
The proposed method is applied to online video foreground and background separation from compressive measurements.
Incorporating Prior Information in Compressive Online Robust Principal Component Analysis
In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements.
Measurement Bounds for Sparse Signal Reconstruction with Multiple Side Information
To address this problem, we theoretically study a generic \textcolor{black}{weighted $n$-$\ell_{1}$ minimization} framework and propose a reconstruction algorithm that leverages multiple side information signals (RAMSI).
