ALICE: Adapt your Learnable Image Compression modEl for variable bitrates

Gabriele Spadaro, Muhammad Salman Ali, Alberto Presta, Giommaria Pilo, Sung-Ho Bae, Jhony H. Giraldo, Attilio Fiandrotti, Marco Grangetto, Enzo Tartaglione

Abstract: When training a Learned Image Compression model, the loss function is minimized such that the encoder and the decoder attain a target Rate-Distorsion trade-off. Therefore, a distinct model shall be trained and stored at the transmitter and receiver for each target rate, fostering the quest for efficient variable bitrate compression schemes. This paper proposes plugging Low-Rank Adapters into a transformer-based pre-trained LIC model and training them to meet different target rates. With our method, encoding an image at a variable rate is as simple as training the corresponding adapters and plugging them into the frozen pre-trained model. Our experiments show performance comparable with state-of-the-art fixed-rate LIC models at a fraction of the training and deployment cost. We publicly released the code at https://github.com/EIDOSLAB/ALICE.