Aug 27, 2022 · Our model compresses images in a coarse-to-fine fashion and supports parallel encoding and decoding, leading to fast execution on GPUs. Code is ...
We pro- pose to use a quantization-aware latent variable model for modern hierarchical VAEs, making practical image coding feasible. We present a powerful and ...
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Authors' PyTorch implementation of lossy image compression methods that are based on hierarchical VAEs - duanzhiihao/lossy-vae.
Recent work has shown a strong theoretical connection between variational autoencoders (VAEs) and the rate dis- tortion theory.
May 25, 2023 · QRes-VAE (Quantized ResNet VAE) is a neural network model for lossy image compression. It is based on the ResNet VAE architecture.
1. INTRODUCTION Block truncation coding (BTC) is a lossy compression technique applicable for gray-scale images, i.e. it reduces the file size but loses some ...
This work redesigns ResNet VAEs using a quantization-aware posterior and prior, enabling easy quantization and entropy coding for image compression and ...
Based on VAEs, we develop a new scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical ...
Feb 2, 2023 · Bibliographic details on Lossy Image Compression with Quantized Hierarchical VAEs.
Jan 5, 2023 · The paper is titled “Lossy Image Compression with Quantized Hierarchical VAEs,” which proposed a novel method for lossy image compression ...