  Learning-based lossy image compression usually involves the joint
optimization of rate-distortion performance. Most existing methods adopt
spatially invariant bit length allocation and incorporate discrete entropy
approximation to constrain compression rate. Nonetheless, the information
content is spatially variant, where the regions with complex and salient
structures generally are more essential to image compression. Taking the
spatial variation of image content into account, this paper presents a
content-weighted encoder-decoder model, which involves an importance map subnet
to produce the importance mask for locally adaptive bit rate allocation.
Consequently, the summation of importance mask can thus be utilized as an
alternative of entropy estimation for compression rate control. Furthermore,
the quantized representations of the learned code and importance map are still
spatially dependent, which can be losslessly compressed using arithmetic
coding. To compress the codes effectively and efficiently, we propose a trimmed
convolutional network to predict the conditional probability of quantized
codes. Experiments show that the proposed method can produce visually much
better results, and performs favorably in comparison with deep and traditional
lossy image compression approaches.
