Optical field image coding method based on parallax prediction

Abstract

The invention discloses an optical field image coding method based on parallax prediction. The method comprises the following steps: converting an optical field image into a pseudo-video sequence; predicting optical flow information between a coded frame and the current frame in the pseudo-video sequence by use of a deep neural network at a coding end, converting the optical flow information into the parallax information, and then performing the motion compensation and computing residual error; finally, writing residual error quantization coding in a code flow; at a decoding end, predicting the optical flow information between the decoded frame and the current frame by use of the deep neural network and a method consistent with the method adopted at the coding end, converting the optical flow information into a motion vector of a corresponding decoding block, obtaining a prediction block of the current decoding block through the motion compensation, and constructing a reconstruction block by use of the residual error information in the code flow. By use of the coding method disclosed by the invention, the motion estimation time can be saved, the bit number for coding the motion vector is lowered, the compression performance of the optical field image is improved, the motion information is unnecessary to write in the code flow, and the bandwidth required by code flow information transmission can be greatly lowered.

Description

technical field [0001] The present invention relates to the technical field of video coding, in particular to a light field image coding method based on parallax prediction. Background technique [0002] The light field camera can not only record the light intensity in the space scene, but also record its direction information. With the launch of the Lytro light field camera, the light field image has great application prospects in the fields of stereoscopic display, virtual reality, and depth calculation. For the light field image, we can convert it into a set of subaperture images (subaperture images), each subaperture image has a small parallax, so their information redundancy is very large. How to efficiently compress sub-aperture images using the parallax between them is an urgent problem to be solved in the application of light field technology. [0003] At present, there are two schemes: 1) Combine parallax compensation into wavelet transform, but this method cannot ...

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Problems solved by technology

[0003] At present, there are two schemes: 1) Combine parallax compensation into wavelet transform, but this method cannot be effectively combined with the existing block-based hybrid coding framework, and this method cannot support real-time operation; 2) Use projection Mapping models the perspective transformation between sub-aperture images, but its compression performance is not high due to the problem of feature point detection accuracy

[0004] On the other hand, although the disparity information in the light field can be obtained by calculating motion vectors, the current calculation process involves motion estimation, which has a high time complexity, and encoding these motion vectors costs a lot of bits

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