17 citations · 29 across the 12 of their papers we have counts for
6 papers · 1 filter
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
Xi Zhang, Xiaolin Wu
It is customary to deploy uniform scalar quantization in the end-to-end optimized Neural image compression methods, instead of more powerful vector quantization, due to the high co…
FLLIC: Functionally Lossless Image Compression
Xi Zhang, Xiaolin Wu
Recently, DNN models for lossless image coding have surpassed their traditional counterparts in compression performance, reducing the previous lossless bit rate by about ten percen…
LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression
Xi Zhang, Xiaolin Wu
Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of…
Dual-layer Image Compression via Adaptive Downsampling and Spatially Varying Upconversion
Xi Zhang, Xiaolin Wu
Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-re…
Deep Decoding of -coded Light Field Images
Muhammad Umair Mukati, Xi Zhang, Xiaolin Wu +1
To enrich the functionalities of traditional cameras, light field cameras record both the intensity and direction of light rays, so that images can be rendered with user-defined ca…
Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding
Xi Zhang, Xiaolin Wu
Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from i…