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    Researchers developed a new perceptual quality model for video-based point cloud compression (V-PCC). This model improves rate-distortion optimization, leading to higher visual quality at the same bitrate.

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    Area of Science:

    • Computer Vision
    • Multimedia Compression
    • Perceptual Quality Assessment

    Background:

    • Rate-distortion optimization is crucial for efficient video-based point cloud compression (V-PCC).
    • Defining a computationally inexpensive quality measure that correlates with perceptual quality is a key challenge.
    • Existing quality measures are insufficient for point cloud data.

    Purpose of the Study:

    • To propose a novel linear perceptual quality model for V-PCC.
    • To enable effective rate-distortion optimization for point cloud compression.
    • To enhance the perceptual quality of compressed point clouds.

    Main Methods:

    • Developed a linear perceptual quality model using V-PCC geometry and color quantization step sizes.
    • Computed model coefficients from two features of the original point cloud.
    • Conducted subjective quality tests on 400 compressed point clouds.

    Main Results:

    • The proposed model showed strong correlation with Mean Opinion Scores (MOS).
    • Outperformed state-of-the-art objective quality measures in correlation analyses (Spearman and Pearson).
    • Rate-distortion optimization using the proposed model yielded higher perceptual quality compared to exhaustive search with objective metrics.

    Conclusions:

    • The proposed linear perceptual quality model effectively predicts subjective quality for V-PCC.
    • This model enables superior rate-distortion optimization, enhancing perceptual quality.
    • The developed datasets are publicly available for further research.