2023/02/03 by Rashid Zamanshoar Heris, Ivan V. Bajić, Heris, Rashid Zamanshoar +1 · 1 citation
Computer Science · #Advanced Data Compression Techniques #Advanced Steganography and Watermarking Techniques #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Video Coding and Compression Technologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2302.02014
openalex publication_date 2023/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the rise of remote work and collaboration, compression of screen content images (SCI) is becoming increasingly important. While there are efficient codecs for natural images, as well as codecs for purely-synthetic images, those SCIs that contain both synthetic and natural content pose a particular challenge. In this paper, we propose a learning-based image coding model developed for such SCIs. By training an encoder to provide a latent representation suitable for two tasks -- input reconstruction and synthetic/natural region segmentation -- we create an effective SCI image codec whose strong performance is verified through experiments. Once trained, the second task (segmentation) need not be used; the codec still benefits from the segmentation-friendly latent representation.