Physics

Image compression

Pattern recognition (psychology)

Codec

JPEG

Discrete cosine transform

Quantization (signal processing)

ENCODE

JPEG 2000

Data compression

Gene

Entropy (arrow of time)

Image (mathematics)

Pixel

Transform coding

Artificial intelligence

Lossy compression

Entropy encoding

Chemistry

Computer science

Quantum mechanics

Lossless compression

Computer hardware

Computer vision

Image quality

Biochemistry

Convolutional neural network

Image processing

We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. Finally, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to multiple metrics. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well as recently published methods based on deep neural networks.

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks