  Light field, as a new data representation format in multimedia, has the
ability to capture both intensity and direction of light rays. However, the
additional angular information also brings a large volume of data. Classical
coding methods are not effective to describe the relationship between different
views, leading to redundancy left. To address this problem, we propose a novel
light field compression scheme based on implicit neural representation to
reduce redundancies between views. We store the information of a light field
image implicitly in an neural network and adopt model compression methods to
further compress the implicit representation. Extensive experiments have
demonstrated the effectiveness of our proposed method, which achieves
comparable rate-distortion performance as well as superior perceptual quality
over traditional methods.
