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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Virtual reality (VR) applications necessitate efficient spherical image compression.
    • Current methods convert spherical images to planar projections (e.g., equirectangular projection), leading to inefficiencies with deep neural network (DNN) based compression due to non-uniform sampling.
    • Existing DNN-based planar compression methods struggle with the inherent properties of spherical data.

    Purpose of the Study:

    • To develop a novel deep neural network (DNN) approach for direct spherical image compression.
    • To overcome the limitations of planar projection-based compression for spherical content.
    • To improve the rate-distortion (R-D) performance in spherical image compression.

    Main Methods:

    • Proposed spherical DNNs utilizing uniform sampling and an ordered rooted tree index (Spherical Measure-Based Spherical Image Representation - SMSIR).
    • Defined spherical convolution and window transformer operations for exploiting local and non-local correlations on the sphere.
    • Introduced SMixFormer, a module integrating spherical convolution and self-attention for enhanced feature extraction.
    • Developed a spherical transformer context model with ordering based on the rooted tree index for improved entropy modeling.

    Main Results:

    • The proposed spherical DNN approach demonstrated superior performance over traditional standards (JPEG, JPEG2000, BPG).
    • Achieved a bitrate reduction exceeding 16% compared to state-of-the-art learning-based hyperprior planar compression models.
    • Experimental results validated the effectiveness of uniform sampling and the SMSIR framework for spherical image compression.

    Conclusions:

    • Spherical DNNs offer a more efficient and effective solution for spherical image compression compared to projection-based methods.
    • The SMixFormer module and spherical transformer context model significantly enhance compression performance.
    • This work advances the field of VR content delivery by enabling higher quality spherical images at lower bitrates.