Image compression

Operating system

Image (mathematics)

Transform coding

Computer science

Rate–distortion optimization

Lossless compression

Quantum mechanics

Encoder

Lossy compression

Video tracking

Computer vision

Entropy (arrow of time)

Algorithm

Data compression ratio

JPEG

Discrete cosine transform

Data compression

Artificial intelligence

Entropy encoding

Object (grammar)

Convolutional neural network

Image processing

Physics

Multiview Video Coding

Lossy image compression is generally formulated as a joint rate-distortion optimization problem to learn encoder, quantizer, and decoder. Due to the non-differentiable quantizer and discrete entropy estimation, it is very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that: (i) the bit rate of the different parts of the image is adapted to local content, and (ii) the content-aware bit rate is allocated under the guidance of a content-weighted importance map. The sum of the importance map can thus serve as a continuous alternative of discrete entropy estimation to control compression rate. The binarizer is adopted to quantize the output of encoder and a proxy function is introduced for approximating binary operation in backward propagation to make it differentiable. The encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner. A...

Learning Convolutional Networks for Content-Weighted Image Compression