3D High Efficiency Video Coding (3D-HEVC) provides a significant potential on increasing the compression ratio of multi-view RGB-D videos. However, the bit rate still rises dramatically with the improvement of the video resolution, which will bring challenges to the transmission network, especially the mobile network. This paper propose an ROI multi-resolution compression method for 3D-HEVC to better preserve the information in ROI on condition of limited bandwidth. This is realized primarily through ROI extraction and compression multi-resolution preprocessed video as alternative data according to the network conditions. At first, the semantic contours are detected by the modified structured forests to restrain the color textures inside objects. The ROI is then determined utilizing the contour neighborhood along with the face region and foreground area of the scene. Secondly, the RGB-D videos are divided into slices and compressed via 3D-HEVC under different resolutions for selection by the audiences and applications. Afterwards, the reconstructed low-resolution videos from 3D-HEVC encoder are directly up-sampled via Laplace transformation and used to replace the non-ROI areas of the high-resolution videos. Finally, the ROI multi-resolution compressed slices are obtained by compressing the ROI preprocessed videos with 3D-HEVC. The temporal and special details of non-ROI are reduced in the low-resolution videos, so the ROI will be better preserved by the encoder automatically. Experiments indicate that the proposed method can keep the key high-frequency information with subjective significance while the bit rate is reduced.
The hyperspectral image in thermal infrared domains provide information, such as temperature and emissivity, about different kinds of materials. These information can be used for a wide number of applications such as mineral mapping, bathymetry, indoor and outdoor detection of chemicals. But because of the limitation of spatial resolution and the characteristics of thermal infrared sensor, there are many mixed pixels in the data, whose temperature，emissivity and abundance of different components can be hard to estimate. In this paper, a new method to estimate the parameters in pure and mixed pixels is proposed based on linear and nonlinear optimization. Firstly, the standard temperature and emissivity separation (TES) algorithm is applied on pure pixels of different materials selected by supervise or unsupervised methods to get the initial temperature. Secondly, the emissivity in different bands can be retrieved by minimizing the reconstruction error, which the more accurate temperature is optimized with. The emissivity in one band is trained by the samples in the same band but in different pixels, while the temperature is trained by different bands in one pixel. Lastly, the abundance and temperature of components in mixed pixels are estimated based on a linear mixture model of the bottom of atmosphere radiance as full constraint linear optimization problem and nonlinear optimization problem. The method is also analyzed with respect to sensitivity to the noise and different parameters’ influences on estimation errors.