CTU-Level Adaptive QP Offset Algorithm for V-PCC Using JND and Spatial Complexity
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Regular Section
CTU-Level Adaptive QP Offset Algorithm for V-PCC Using JND and Spatial Complexity
Mengmeng ZHANGZeliang ZHANGYuan LIRan CHENGHongyuan JINGZhi LIU
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JOURNAL FREE ACCESS

2024 Volume E107.A Issue 8 Pages 1400-1403

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Abstract

Point cloud video contains not only color information but also spatial position information and usually has large volume of data. Typical rate distortion optimization algorithms based on Human Visual System only consider the color information, which limit the coding performance. In this paper, a Coding Tree Unit (CTU) level quantization parameter (QP) adjustment algorithm based on JND and spatial complexity is proposed to improve the subjective and objective quality of Video-Based Point Cloud Compression (V-PCC). Firstly, it is found that the JND model is degraded at CTU level for attribute video due to the pixel filling strategy of V-PCC, and an improved JND model is designed using the occupancy map. Secondly, a spatial complexity detection metric is designed to measure the visual importance of each CTU. Finally, a CTU-level QP adjustment scheme based on both JND levels and visual importance is proposed for geometry and attribute video. The experimental results show that, compared with the latest V-PCC (TMC2-18.0) anchors, the BD-rate is reduced by -2.8% and -3.2% for D1 and D2 metrics, respectively, and the subjective quality is improved significantly.

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© 2024 The Institute of Electronics, Information and Communication Engineers
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