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Plenoptic Image Coding using Macropixel-based Intra Prediction.

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This study introduces a novel macropixel-based intra prediction method for coding super high-resolution plenoptic images. The technique significantly reduces bitrate, outperforming HEVC and other advanced methods.

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Area of Science:

Computer Vision
Image Processing
Video Coding

Background:

Plenoptic images capture spatial and angular light radiance using macropixels.
Efficient coding of high-resolution plenoptic data is challenging.
Existing methods struggle to fully exploit spatial correlations within macropixel structures.

Purpose of the Study:

To propose a macropixel-based intra prediction method for enhanced plenoptic image coding.
To improve compression efficiency for super high-resolution plenoptic images.
To leverage spatial correlations among pixels within neighboring microlenses.

Main Methods:

An invertible image reshaping method aligns macropixels with block-based coding grids.
A video encoder incorporates three novel intra prediction modes: MWP, CSP, and BMP.
Predictions are generated by minimizing spatial Euclidean distance and boundary errors.

Main Results:

The proposed method achieves an average bitrate reduction of 47.0% compared to HEVC.
Significant bitrate savings of 45.0%, 27.7%, and 22.7% are observed against PVTA, IBC, and LLE methods, respectively.
The approach effectively exploits spatial correlations for improved coding performance.

Conclusions:

The macropixel-based intra prediction method offers superior compression efficiency for plenoptic images.
This technique provides a significant advancement in plenoptic image coding standards.
The proposed modes effectively utilize spatial information for substantial bitrate reduction.