Learning Optimal Linear Block Transform by Rate Distortion Minimization

Alessandro Gnutti, Chia-Hao Kao, Wen-Hsiao Peng, Riccardo Leonardi

Abstract: The rise of deep learning has spurred advancements in image compression, with end-to-end learned systems gaining traction. However, their adoption in standard frameworks is limited, as they require a major overhaul of existing hardware designed for traditional methods. Moreover, their computational complexity, especially on the decoder side, remains significantly higher than conventional codecs. Consequently, optimizing traditional codecs remains a key research focus.

External IDs:dblp:conf/dcc/GnuttiKPL25