  Recently, learned image compression methods have developed rapidly and
exhibited excellent rate-distortion performance when compared to traditional
standards, such as JPEG, JPEG2000 and BPG. However, the learning-based methods
suffer from high computational costs, which is not beneficial for deployment on
devices with limited resources. To this end, we propose shift-addition parallel
modules (SAPMs), including SAPM-E for the encoder and SAPM-D for the decoder,
to largely reduce the energy consumption. To be specific, they can be taken as
plug-and-play components to upgrade existing CNN-based architectures, where the
shift branch is used to extract large-grained features as compared to
small-grained features learned by the addition branch. Furthermore, we
thoroughly analyze the probability distribution of latent representations and
propose to use Laplace Mixture Likelihoods for more accurate entropy
estimation. Experimental results demonstrate that the proposed methods can
achieve comparable or even better performance on both PSNR and MS-SSIM metrics
to that of the convolutional counterpart with an about 2x energy reduction.

Image and Video Processing;Computer Vision and Pattern Recognition