论文标题
残留CNN辅助JPEG的光场压缩
Light Field Compression by Residual CNN Assisted JPEG
论文作者
论文摘要
光场（LF）成像由于最近在3维（3D）显示和渲染以及增强和虚拟现实使用方面的成功而引起了极大的关注。但是，由于两个额外的维度，LFS比传统图像大得多。我们开发了一种基于JPEG的基于学习的技术，可以从JPEG bitstream中重建LF，平均每个像素比为0.0047。为了进行压缩，我们保持LF的中心视图，并以50％的质量使用JPEG压缩。我们的重建管道由一个小的JPEG增强网络（JPEG-HANCE）组成，该网络是一个深度估计网络（DEPTH-NET），然后是通过扭曲增强的中心视图来综合视图综合。我们的管道比在压缩和减压方面从LF中提取的伪序列上使用视频压缩要快得多，同时保持有效的性能。我们表明，与用于压缩的最新视频压缩技术相比，通过重建的LFS进行1％的压缩时间成本和减压的18倍加速，我们的方法具有更好的结构相似性指数（SSIM）和可比的峰值信噪比（PSNR）。
Light field (LF) imaging has gained significant attention due to its recent success in 3-dimensional (3D) displaying and rendering as well as augmented and virtual reality usage. Nonetheless, because of the two extra dimensions, LFs are much larger than conventional images. We develop a JPEG-assisted learning-based technique to reconstruct an LF from a JPEG bitstream with a bit per pixel ratio of 0.0047 on average. For compression, we keep the LF's center view and use JPEG compression with 50% quality. Our reconstruction pipeline consists of a small JPEG enhancement network (JPEG-Hance), a depth estimation network (Depth-Net), followed by view synthesizing by warping the enhanced center view. Our pipeline is significantly faster than using video compression on pseudo-sequences extracted from an LF, both in compression and decompression, while maintaining effective performance. We show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs.
