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IMAGE SEGMENTATION FOR IMPROVED LOSSLESS SCREEN CONTENT COMPRESSION

DOI:
10.60864/2q0m-4j92
Citation Author(s):
Tilo Strutz, Hannah Och, André Kaup
Submitted by:
Shabhrish Uddehal
Last updated:
17 November 2023 - 12:07pm
Document Type:
Presentation Slides
Document Year:
2023
Event:
Presenters:
Shabhrish Reddy
Paper Code:
4590
 

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two subimages (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate
reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.

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