Neural Multi-scale Image Compression
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables and the parallel multi-scale lossless coder enables rapid and accurate lossless coding of the quantized variables via encoding/decoding the variables in parallel. Our proposed model achieves comparable performance to the state-of-the-art model on Kodak and RAISE-1k dataset images, and it encodes a PNG image of size $768 \times 512$ in 70 ms with a single GPU and a single CPU process and decodes it into a high-fidelity image in approximately 200 ms.
Code (0)
등록된 구현이 없습니다.
Tasks
CPUGPUImage CompressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain
Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective…
DecoderImage CompressionPatch MatchingLossless Image Compression Using a Multi-Scale Progressive Statistical Model
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have been used in …
Image CompressionMulti-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression
Autoencoder-based structures have dominated recent learned image compression methods. However, the inherent information loss associated with autoencoders limits their rate-distortion performance at high bit rates and res…
Image CompressionFD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression
The learned image compression (LIC) methods have already surpassed traditional techniques in compressing natural scene (NS) images. However, directly applying these methods to screen content (SC) images, which possess di…
Image CompressionMS-SSIMQuantizationSSIMCOMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability
Recently, neural network (NN)-based image compression studies have actively been made and has shown impressive performance in comparison to traditional methods. However, most of the works have focused on non-scalable ima…
Image Compression