Neural Multi-scale Image Compression
This is an incremental improvement for image compression applications, offering faster processing times while maintaining competitive quality.
The paper tackles image compression by proposing a neural multi-scale method using a lossy autoencoder and parallel lossless coder, achieving comparable performance to state-of-the-art models on Kodak and RAISE-1k datasets with encoding times of 70 ms and decoding times of 200 ms for a 768x512 PNG image.
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.