4D Epanechnikov Mixture Regression in LF Image Compression
نویسندگان
چکیده
With the emergence of light field imaging in recent years, compression its elementary image array (EIA) has become a significant problem. Our coding framework includes modeling and reconstruction. For modeling, covariance-matrix form 4-D Epanechnikov kernel (4-D EK) correlated statistics were deduced to obtain mixture models EMMs). A regression EMR) was proposed based on this EK, adaptive model selection AMLS) algorithm designed realize optimal for pseudo video sequence (PVS) extracted key-EIA. linear function reconstruction (LFBR) correlation between adjacent images (EIs). The decoded realized clear outline superior efficiency compared high-efficiency (HEVC) JPEG 2000 below approximately 0.05 bpp. This work an unprecedented theoretical application by (1) proposing theory, (2) exploiting images, (3) using number models, (4) employing function-based according content similarity.
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ژورنال
عنوان ژورنال: IEEE Transactions on Circuits and Systems for Video Technology
سال: 2022
ISSN: ['1051-8215', '1558-2205']
DOI: https://doi.org/10.1109/tcsvt.2021.3104575