A study on efficient compression of multi-focus images for dense Light-Field reconstruction

Takashi Sakamoto, K. Kodama, T. Hamamoto
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引用次数: 26

Abstract

Light-Field enables us to observe scenes from free viewpoints. However, it generally consists of 4-D enormous data, that are not suitable for storing or transmitting without effective compression. 4-D Light-Field is very redundant because essentially it includes just 3-D scene information. Actually, although robust 3-D scene estimation such as depth recovery from Light-Field is not so easy, a method of reconstructing Light-Field directly from 3-D information composed of multi-focus images without any scene estimation is successfully derived. Previously, based on the method, Light-Field compression via synthesized multi-focus images as effective representation of 3-D scenes was proposed. In this paper, we study efficient compression of multi-focus images synthesized from dense Light-Field by using DWT instead of DCT-based compression in order to suppress degradation such as block noise. Quality of reconstructed Light-Field is evaluated by PSNR and SSIM for analyzing characteristics of residuals. Experimental results reveal that our method is much superior to Light-Field compression using disparity-compensation at low bit-rate.
密集光场重建中多聚焦图像的有效压缩研究
光场使我们能够从自由视点观察场景。然而,它通常由4-D庞大的数据组成,如果没有有效的压缩,就不适合存储或传输。4-D光场是非常冗余的,因为本质上它只包含3-D场景信息。实际上,虽然光场深度恢复等鲁棒三维场景估计并不容易实现,但我们成功地推导出了一种直接从多焦图像组成的三维信息中重建光场的方法。在此基础上,提出了利用合成的多聚焦图像进行光场压缩,作为三维场景的有效表示。在本文中,我们研究了用DWT代替基于dct的压缩对密集光场合成的多聚焦图像进行有效压缩,以抑制块噪声等退化。利用PSNR和SSIM评价重建光场的质量,分析残差特征。实验结果表明,在低比特率下,我们的方法比使用差值补偿的光场压缩要好得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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