3D Image And Video Coding Based On Compressive Sensing

With the broadcasting of the three-dimensional (3-D) film.3-D video has drawn significant attention among industry and academic researchers, which has widely used in3-D TV, video conference, remote video monitoring, education, medical treatment, military and many other areas. It has greatly enriched the content of the media and offered the consumers an immersive multimedia experience. However, with the increasing number of the viewpoint, the volume of3-D video data is growing. In order to avoid the surge of the data in channel, the data must be compressed effectively. The theory of compressive sensing (CS) successfully broke through the limitation of the traditional temporal sampling theory that signals can be recovered without distortion on condition that the signals are sampled at a rate at least twice the highest frequency in the signals. CS indicates that the sampling frequency is no longer depended on the bandwidth of the signal, but depended on the structure and content. It processes a new way for the signal acquisition. In this paper, CS is used in the coding of3-D video, and the main research results are as follows:(1) When the image is compressed and reconstructed based on CS theory at the same sampling rate, the low sampling rate can’t ensure that every block in the image obtains the well reconstruction quality and the high sampling rate usually leads to the resource waste due to the different sparseness degree of each block in the image. In order to solve the above problem, an adaptive depth map coding algorithm based on CS is proposed in this paper. In the scheme, the proportion of the edge for each image block is used as the principle to estimate the sparseness degree, then according to the sparseness degree, different sampling rate were chosen adaptively for each block, thus a higher reconstruction quality is acquired for the depth map at a lower sampling rate;(2) Image perceptual quality is largely influenced by visual attention. In this paper a saliency-based compressive sampling scheme is proposed in which the saliency is applied in the process of sampling based on the characteristic of human visual system. In the scheme the saliency detection of the depth map is first used, and then different sampling rate were allocated according to the saliency information to improve the rate distortion performance; (3) The application of traditional video coding method, due to its highly complex processes in the video coding side and its requirement for stable channel, is limited in the channel which is less stable and in the mobile intelligent terminals whose computing resources and energy consumption are constrained. Distributed video coding and compressive sensing can shift the complexity from encoder to decoder, so they are combined together in the system of distributed compressive sensing in this paper. Video frames in each view are split into key frames and CS frames according to the value of GOP. Then they are encoded independently and decoded jointly. Thus on the premise of guaranteeing the reconstructed video quality greatly reduces the complexity in the encoder.