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    This study introduces a latitude adaptive coding scheme for 360° image compression using deep neural networks (DNNs). The novel approach improves compression efficiency by allocating codes based on latitude, outperforming existing standards.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • 360° images are crucial for virtual reality, but current compression methods struggle with equirectangular projection (ERP) due to unbalanced sampling.
    • Deep neural network (DNN) based codecs face inefficiencies with ERP images, necessitating new approaches.

    Purpose of the Study:

    • To develop a latitude adaptive coding scheme for DNN-based 360° image compression.
    • To enhance compression efficiency by addressing the unbalanced sampling problem in ERP images.

    Main Methods:

    • Introduced a latitude adaptive coding scheme allocating variable codes per region based on latitude.
    • Implemented a flexible regional adaptive rate loss for rate control and latitude adaptive constraints.
    • Incorporated viewport-based distortion loss to optimize perceived quality.

    Main Results:

    • The proposed scheme significantly improves 360° image compression efficiency.
    • Achieved approximately 15% bit savings compared to a baseline learned image compression model.
    • Outperformed established standards like JPEG, JPEG2000, HEVC Intra, and VVC Intra Coding.

    Conclusions:

    • The latitude adaptive coding scheme offers a superior solution for DNN-based 360° image compression.
    • This method effectively mitigates issues associated with ERP image sampling, leading to better performance.