4D Epanechnikov Mixture Regression in LF Image Compression

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 covariancematrix 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.

Geometry-Aware View ReconstructionNetwork for Light Field ImageCompression

“…Three different prediction methods were respectively utilized for different categories, which acquired a high prediction accuracy. In order to remove redundancies of lenslet image, Liu et al [38] introduced the 4D Epanechnikov mixture regression into LF compression, where a 4D Epanechnikov mixture regression and a linear function-based reconstruction were adopted for high-efficiency LF compression.…”

Geometry-aware view reconstruction network for light field image compression

“…Three different prediction methods were respectively utilized for different categories, which acquired a high prediction accuracy. In order to remove redundancies of lenslet image, Liu et al 40 introduced the 4D Epanechnikov mixture regression into LF compression, where a 4D Epanechnikov mixture regression and a linear function-based reconstruction were adopted for high-efficiency LF compression.…”