For the sake of providing 3D contents for up-coming 3D display devices, a real-time automatic depth fusion
2D-to-3D conversion system is needed on the home multimedia platform. We proposed a priority depth fusion
algorithm with a 2D-to-3D conversion system which generates the depth map from most of the commercial video
sequences. The results from different kinds of depth reconstruction methods are integrated into one depth map
by the proposed priority depth fusion algorithm. Then the depth map and the original 2D image are converted
to stereo images for showing on the 3D display devices. In this paper, a 2D-to-3D conversion algorithm set
is combined with the proposed depth fusion algorithm to show the improved results. With the converted 3D
contents, the needs for 3D display devices will also increase. As long as the two technologies evolve, the 3D-TV
era will come as soon as possible.
Luminance and chrominance correction (LCC) is important in multi-view video coding (MVC) because it provides
better rate-distortion performance when encoding video sequences captured by ill-calibrated multi-view cameras. This
paper presents a robust and fast LCC algorithm based on motion compensated linear regression which reuses the
motion information from the encoder. We adopt the linear weighted prediction model in H.264/AVC as our LCC
model. In our experiments, the proposed LCC algorithm outperforms basic histogram matching method up to 0.4dB
with only few computational overhead and zero external memory bandwidth. So, the dataflow of this method is
suitable for low bandwidth/low power VLSI design for future multi-view applications.