Study On Coding Of Elemental Images In Integral Imaging

Integral Imaging is an autostereoscopic3D display technology with the characteristicsof full color、continuous viewpoints，which is able to reproduce realistic3D scenes. Due to alarge amount of image data collected, Efficient compression to facilitate storage andtransmission is necessary. There are there common representation methods for a still imagecaptured by Integral Imaging: the direct captured element image array, the sub-image array,the ray-space image array. The sum-image array and ray-space image array can be obtainedfrom the element image array. This article describes a new type of representation, here called"spliced image array," and also obtained from the element image array. The beam overlapcondition of adjacent element images or adjacent sub-images from lens array and acquisitionpanel is analyzed. The beam overlap condition of adjacent images is one of the reasonswhich affect the similarity between adjacent images. The result shows that beam overlapratio of adjacent element images is positively correlated with the depth of3D object, so thesimilarity between adjacent element images may be increased. And beam overlap ratio ofadjacent sum-images is inverse correlated with the depth of3D object, so the similaritybetween adjacent sub-images may be reduced.When treating a still image captured by Integral Imaging in different ways, Thecompressing methods of the image may be different. The still image can be treated as awhole, one ordinary2D image with periodic repetitive texture. Or it can be treated as animage array(element image array or sub-image array or ray-space image array or splicedimage array). To compress an image array, there are several main methods: using transformaccording to the characteristics of image array; scanning the image array intoone-dimensional (1D) pseudo-video stream with scanning curve; using two-dimensional (2D)spatial prediction structure; compressed sensing and other methods. Scanning the imagearray into1D pseudo-video stream with scanning curve is equivalent to1D spatial predictionstructure, which only use the correlation of the image array in one direction(horizontal orvertical), therefore, the compression effect is not very good.2D spatial prediction structureuse the correlation of the image array in two direction(horizontal and vertical) at the sametime, therefore, the compression effect is better. This article uses2D spatial predictionstructure on spliced image array, compared with the1D pseudo video streaming method andseveral other2D spatial structure prediction methods, and the proposed method is better atleast for the test images.The compressing methods of one Integral Video also may be different when treating one still image captured by Integral Imaging in different ways. When one still image istreated as a whole, one ordinary2D image with periodic repetitive texture, a common2Dvideo compression schemes, such as: MPEG-2/H.264/HEVC can be used. When one stillimage is treated as an image array (element image array or sub-image array or ray-spaceimage array or spliced image array), the multi-view video method compression methods canbe used, and each image in image array is treated as one image in one viewpoint. This articletransform the Integral Video composed of element image array into the form composed ofspliced image array, then compress the Integral Video with the method for multi-view video，and one spliced image is treated as one image in one viewpoint. Compared with the directuse of HEVC compression method on Integral Video composed of element image array, theproposed method is better at least for the test images.