Gaussian Guided Inter Prediction for Focal Stack Images Compression
| Autor: | You Yang, Yaguang Yin, Kejun Wu, Qiong Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Point spread function
Motion compensation Computer science Gaussian ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology External Data Representation Residual distribution symbols.namesake Redundancy (information theory) Motion estimation 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Algorithm Light field |
| Zdroj: | DCC |
| DOI: | 10.1109/dcc47342.2020.00014 |
| Popis: | Focal stack is an intermediate data representation obtained by projecting 4D light field (LF) in z-dimension. This kind of representation is fundamental for future interactive and immersive visual applications. However, focal stack images are a series of samples focused at varying depths of static scenes, which yields considerable redundancy among them. In this paper, we propose a Gaussian guided inter prediction model to eliminate the visual redundancy. In our work, the effect of the varying focus distance on plenoptic imaging system is characterized by the point spread function (PSF), and we propose a simplified Gaussian-like PSF to fit this characteristic according to the features of focal stack images. After that, Gaussian guided motion estimation and motion compensation are both implemented in this model. Experimental results show that our model can achieve smaller residual distribution. There are 10.33% bit rate saving and 0.397 dB PSNR gain on average in three configurations compared with HEVC anchor. Particularly, it brings about up to 16.60% bit rate saving with 0.649 dB PSNR increment in Low Delay P configuration. |
| Databáze: | OpenAIRE |
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