4-D Epanechnikov Mixture Regression in Light Field Image Compression

TL;DR

This paper introduces a novel 4-D Epanechnikov mixture regression framework for light field image compression, achieving superior efficiency and reconstruction quality compared to existing standards.

Contribution

It proposes the first 4-D Epanechnikov kernel theory and applies it to light field image modeling, with adaptive model selection and linear reconstruction methods.

Findings

Achieved better coding efficiency than HEVC and JPEG 2000 at 0.05 bpp.

Developed a new 4-D Epanechnikov mixture regression model.

Demonstrated clear outline reconstruction with superior quality.

Abstract

With the emergence of light field imaging in recent years, the compression of its elementary image array (EIA) has become a significant problem. Our coding framework includes modeling and reconstruction. For the modeling, the covariance-matrix form of the 4-D Epanechnikov kernel (4-D EK) and its correlated statistics were deduced to obtain the 4-D Epanechnikov mixture models (4-D EMMs). A 4-D Epanechnikov mixture regression (4-D EMR) was proposed based on this 4-D EK, and a 4-D adaptive model selection (4-D AMLS) algorithm was designed to realize the optimal modeling for a pseudo video sequence (PVS) of the extracted key-EIA. A linear function based reconstruction (LFBR) was proposed based on the correlation between adjacent elementary images (EIs). The decoded images realized a clear outline reconstruction and superior coding efficiency compared to high-efficiency video coding (HEVC) and JPEG 2000 below approximately 0.05 bpp. This work realized an unprecedented theoretical application by (1) proposing the 4-D Epanechnikov kernel theory, (2) exploiting the 4-D Epanechnikov mixture regression and its application in the modeling of the pseudo video sequence of light field images, (3) using 4-D adaptive model selection for the optimal number of models, and (4) employing a linear function-based reconstruction according to the content similarity.