In the context of still image coding, our work concerned the study of optimal noisy source coding/denoising and has been done during M. Carlavan’s PhD (2010–2013) in collaboration with the CNES Toulouse and Thales Alénia Space (TAS) in Cannes. Most of the bibliography in this domain is based on the fact that the noisy image should be first optimally denoised and this denoised image should then be optimally coded. In many applications however, the layout of the acquisition imaging chain is fixed and cannot be changed, that is a denoising step cannot be inserted before coding. In this configuration, we showed on a simple case how to express the global distortion as a function of the coding and denoising parameters in the case the denoising step is performed after coding/decoding. We showed that the joint optimized distortion slightly outperforms the disjoint optimized distortion on several satellite test images of the post-Pleiades generation. This result appears then to be very significant for future CNES Space missions. High Efficiency Video Coding (HEVC) recently becomes the video compression standard to succeed H.264/AVC. Considering HEVC as video coding basis, we developed in our work a novel concept of smart video decoding. Some contributions were addressed during the Khoa Vo Nguyen’s PhD (2012–2015) in collaboration with Orange-Labs Issy-Les-Moulineaux. General smart coding and decoding schemes were proposed to remove the limit of conventional coding schemes which is related to the increased number of available coding modes. In that context, we developed a novel video coding framework with assisting supervised machine learning algorithms that aim to efficiently compress video by providing bitrate savings. The idea is to predict optimal coding mode of a current block using classification techniques based on already reconstructed frames. A first practical application in HEVC test model software HM12 reports promising and interesting average bitrate savings.