散焦结合相关性评价算法是利用四维光场的空间信息和角度信息来完成深度估计,该算法建立的散焦响应函数存在求取二阶导方向有限且二阶导求和能量正负相互抵消的问题,这限制了其在复杂场景中的深度重建精度。针对该问题,对散焦响应函数进行了改进,提出基于能量增强散焦响应的光场深度估计算法。该算法充分考虑了周边像素点对当前位置散焦度的影响,通过增加具有权重差异的二阶导个数和方向来实现散焦响应的能量增强。通过实验验证了所提方法的有效性。针对一些边缘复杂的光场图像,所提算法获取的深度图视觉效果更好;与散焦评价结合相关性评价算法相比,改进算法获得深度图像的均方根误差平均减小3.95%。

虚拟结构光编码算法将三维(3D)数据编码成二维(2D)彩色条纹图像的相位信息,以实现数据压缩存储。在将2D彩色条纹图像解码成3D数据时,需要计算条纹相位并进行相位展开,最后映射为3D数据。2D图像的压缩存储会使相位级次索引图出现边缘量化错误,导致3D数据z方向信息解码错误。针对这一问题,改进了虚拟结构光3D数据解码过程,提出了一种有效的局部相位误差修正算法,以提高3D数据的解码精度。数据解码过程中,建立算法,得到绝对相位展开与相对相位展开的准确差值,利用两者差值固定的特点,对其突变区域建立相位补偿机制,抑制相位级次索引图边缘量化误差而引起的3D数据解码错误。实验验证了所提算法的有效性,对编码得到的2D图像压缩存储后,改进的数据解码方法可有效提高虚拟结构光3D数据编码算法的稳健性,与中值滤波算法相比,所提方法可将数据解码方均根误差平均降低9.7%。

Nowadays, distributed source coding (DSC) and distributed video coding (DVC) have been receiving more and more attention due to the distinct contributions to the easy encoding. At the same time, with more new requirements coming forth in the current network communication, the scalability of bit stream has been a new focus in the real applications. A scalable DVC scheme is presented without requiring layered coding in which the main attributions of DVC, namely the capabilities of easy encoding and robustness, are inherited remarkably and the property of scalability is also integrated simultaneously. Based on the block Slepian-Wolf set partitioning in hierarchical trees (SW-SPIHT), the Wyner-Ziv frames are encoded to get the scalable bit stream. In addition, the binary motion searching is explored at the decoder with the help of a rate-variable 'hash' from the encoder to improve the performance of the whole system. The final experimental results show that our system has higher peak signal-to-noise ratio (PSNR) than the pixel-domain DVC at the high bit rate. What is more, the scalability in signal-to-noise ratio (SNR) is also achieved satisfactorily.