Rate Control Based on Similarity Analysis in Multi-view Video Coding

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Presented at CRYPIS 2020 by

Tao Yan

In-ho Ra

Shaojie Hou

Jingyu Feng

And Wu

The joint video expert group proposed a JMVM reference model for multi-view video coding, but the model did not give an effective rate control scheme. This paper proposes a rate control algorithm for multi-view video coding (MVC) based on correlation analysis. The core of the algorithm is to first divide all images into six types of coded frames according to the structural relationship between disparity prediction and motion prediction, which improve the binomial rate distortion model, and then perform the analysis between different views based on similarity analysis. The bit rate control is divided into a four-layer structure for bit rate control of multi-view video coding. Among them, the frame-layer code rate control considers the layer B frame and other factors to allocate the code rate, and the basic unit-layer code rate control uses different quantization parameters according to the content complexity of the macroblock. The average error between the actual bit rate and the target bit rate of the bit rate control algorithm can be controlled by 0.94%.