A layered light-field display based on light-field factorization is considered. In the original work, the factorization is formulated under the assumption that the light field is captured with orthographic cameras. In this paper, we introduce a generalized framework for light-field factorization that can handle both the orthographic and perspective camera projection models. With our framework, a light field captured with perspective cameras can be displayed accurately.

We show that distributed source coding of multi-view images in camera sensor networks (CSNs) using adaptive modules can come close to the Slepian-Wolf bound. In a systematic scenario with limited node abilities, work by Slepian and Wolf suggest that it is possible to encode statistically dependent signals in a distributed manner to the same rate as with a system where the signals are jointly encoded. We considered three nodes (PN, CN and CNs), which are statistically depended. Different distributed architecture solutions are proposed based on a parent node and child node framework. A PN sends the whole image whereas a CNs/CN only partially, using an adaptive coding based on adaptive module-operation at a rate close to theoretical bound - H(CNs|PN)/H(CN|PN,CNs). CNs sends sub-sampled image and encodes the rest of image, however CN encodes all image. In other words, the proposed scheme allows independent encoding and jointly decoding of views. Experimental results show performance close to the information-theoretic limit. Furthermore, good performance of the proposed architecture with adaptive scheme shows significant improvement over previous work.

This Letter proposes a new three dimensional (3D) visual communication approach based on the image-based rendering. We first compactly represent a reference view set by exploiting its geometric correlation and then efficiently compress the representation with appropriate coding schemes. Experimental results demonstrate that our proposed method significantly reduces the required bitrate.

This paper surveys the results of various studies on 3-D image coding. Themes are focused on efficient compression and display-independent representation of 3-D images. Most of the works on 3-D image coding have been concentrated on the compression methods tuned for each of the 3-D image formats (stereo pairs, multi-view images, volumetric images, holograms and so on). For the compression of stereo images, several techniques concerned with the concept of disparity compensation have been developed. For the compression of multi-view images, the concepts of disparity compensation and epipolar plane image (EPI) are the efficient ways of exploiting redundancies between multiple views. These techniques, however, heavily depend on the limited camera configurations. In order to consider many other multi-view configurations and other types of 3-D images comprehensively, more general platform for the 3-D image representation is introduced, aiming to outgrow the framework of 3-D "image" communication and to open up a novel field of technology, which should be called the "spatial" communication. Especially, the light ray based method has a wide range of application, including efficient transmission of the physical world, as well as integration of the virtual and physical worlds.

This paper is concerned with the data compression and interpolation of multi-view image set. In this paper, we propose a novel disparity compensation scheme based on geometric relationship. We first investigate the geometric relationship between a point in the object space and its projection onto view images. Then, we propose the disparity compensation scheme which utilize the geometric constraints between view images. This scheme is used to compress the multi-view image into the structure of the triangular patches and the texture data on the surface of patches. This scheme not only compresses the multi-view image but also synthesize the view images from any viewpoints in the viewing zone. Also, this scheme is fast and have compatibility with 2-D interframe coding. Finally, we report the experiment, where two sets multi-view image were used as original images and the amount of data was reduced to 1/19 and 1/20 with SNR 34 dB and 20 dB, respectively.