Light Field Image Compression Method Based on Linear Reconstruction

Abstract

The invention discloses a light-field image compression method based on linear reconstruction. The light-field image compression method comprises the steps of decomposing a light-field image into viewing angle image arrays and then dividing into A and B sets; at an encoding end, performing compression on viewing angle images in the A set by adopting a first video encoder, transmitting a code stream to a video decoder at the encoding end and a video decoder at a decoding end, obtaining a relationship between viewing angle images in the B set and the viewing angle images in the A set by combining the viewing angle images in the B set with viewing angle images reconstructed by the video decoder at the encoding end in the A set and utilizing a linear reconstruction theory of light-field viewing angle images, and transmitting the relationship to a second video decoder at the decoding end; at the decoding end, reconstructing the B set by utilizing the linear reconstruction theory of the light-field viewing angle images and combining with decoding results of the first and second video decoders at the decoding end; and re-forming the light-field image by utilizing the reconstructed A set and B set. According to the method, encoding data at the encoding end can be greatly reduced, and are reconstructed at the decoding end with a relatively good quality.

Description

technical field [0001] The invention relates to the technical field of light field image compression, in particular to a linear reconstruction-based light field image compression method. Background technique [0002] The light field camera can record the light intensity and direction information (ie light field information) in the space scene. Inside a light-field camera, a microlens array is placed in front of the CCD sensor, near the focal length of the main lens. Each microlens corresponds to a pixel block (ie, macropixel), and the light field image consists of N*M macropixels, where N and M are the height and width of the microlens array, respectively. If each microlens is regarded as a small hole, the back of the main lens forms an inverted image on each macro pixel—that is, each macro pixel can be regarded as a sampling of the main lens in a specific direction. [0003] Under the same spatial resolution, the amount of data is hundreds of times that of traditional 2D ...

AI Technical Summary

Problems solved by technology

Due to the high dimensionality of the light field and the large amount of data, this method has the disadvantages of high complexity and long decoding time

Images