Improving The Reconstruction Quality by Overfitted Decoder Bias in Neural Image Compression
Year: 2022
Type: conference-paper
Abstract: End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images. Since the parameters of these models are learned over large training sets, they are not optimal for any given image to be compressed. In this paper, we propose an instance-based fine-tuning of a subset of decoder’s bias to improve the reconstruction quality in exchange for extra encoding time and minor additional signaling cost. The proposed method is applicable to any end-to-end compression methods, improving the state-of-the-art neural image compression BD-rate by 3 – 5%.
Source: Picture Coding Symposium
Authors: Oussama Jourairi, Muhammet Balcilar, Anne Lambert, Francois Schnitzler
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4317600293