Abstract
As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Rate-distortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).
Highlights
Image or video compression is widely used to facilitate real-time medical data communication in mobile healthcare applications
We proposed four input features which are named as Ratio of Block Size (RBS), Optimal Binary Tree (BT) Direction (OBD), Ratio of the Number of Direction (RND), and Ternary Tree (TT) Indication (TTI), respectively, as described in Tab. 1
In the class A (3,840 × 2,160) and B (1,920 × 1,080) sequences of Joint Video Experts Team (JVET) Common Test Conditions (CTC), the proposed method was evaluated under All Intra (AI) configuration and we compared our method with the previous method [22], where VVC Test Model (VTM) 10.0 was used as an anchor
Summary
Image or video compression is widely used to facilitate real-time medical data communication in mobile healthcare applications. This technology has several applications, including remote diagnostics and emergency incidence responses, as shown in Fig. 1 [1]. Mobile healthcare is one of the key aspects of telemedicine in which clinicians perform a broad range of clinical tasks remotely. Patients communicate with clinicians on hand-held devices with a. There is a need to develop a low-complexity video codec that achieves minimum bitrates with better coding performance while still maintaining high perceptual quality for seamless delivery of medical data within a constrained network bandwidth
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