5 citations · 5 across the 5 of their papers we have counts for
5 papers
C-INR: Customized Convolutional Implicit Neural Representation
Jinglei Shi, Xinran Chang, Jiaqi Cui +3
Implicit Neural Representation (INR) leverages neural networks to represent discrete signals such as images as continuous ones, where the network weights serve as a compact form of…
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Jinglei Shi, Yihong Xu, Christine Guillemot
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations. It offers a more immersive percepti…
Learning-based Spatial and Angular Information Separation for Light Field Compression
Jinglei Shi, Yihong Xu, Christine Guillemot
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this contex…
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression
Jinglei Shi, Christine Guillemot
We propose in this paper a Quantized Distilled Low-Rank Neural Radiance Field (QDLR-NeRF) representation for the task of light field compression. While existing compression methods…
A learning-based view extrapolation method for axial super-resolution
Zhaolin Xiao, Jinglei Shi, Xiaoran Jiang +1
Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in…