Transforms are a key element in block-based video coding systems which, in conjugation with quantization, is important for the overall compression efficiency of the system. This thesis explores multiple transform- based learning schemes. A first contribution of this work is dedicated to the evaluation of transform learning schemes with two flavors 1) online learning, and 2) offline learning. The two approaches are compared against each other and their respective appropriability is studied in detail. Some novel techniques are proposed in this work to 1) improve the stability of the learning scheme and 2) to reduce the signaling cost. In a second contribution of this thesis, the offline multiple-transform learning schemes already known in the literature are further extended with the aims to altogether 1) provide more generic transforms that are less biased towards specific classes of contents, 2) achieve higher compression gains, 3) reduce encoding and decoding computational complexity. An improved Mode Dependent Transform Competition (IMDTC) scheme is proposed which provides a considerable gain of over 5% compared to standard HEVC under All Intra (AI) configuration at a complexity just 2.9 times the standard HEVC. Finally, the content adaptability aspect of the offline learning is explored through a novel content-adapted pool-based transform learning approach where several multiple-transform sets are learned on different contents and pooled together. During the coding of a given region of an image, one transform set is selected locally from the pool. Numerical results show the high potential of this approach against the conservative online and offline approaches.