Frame rate up-conversion motion estimation method and frame rate up-conversion motion estimation system based on Kalman filtering

Abstract

The invention discloses a frame rate up-conversion motion estimation method and a frame rate up-conversion motion estimation system based on Kalman filtering. The method comprises the following steps: first, the parameters and the initial value of state of a Kalman filtering model are set to make the model accord with the actual system; then, the observed value of a motion vector is obtained through a strategy in which unidirectional motion estimation is carried out first and then a unidirectional motion vector is mapped to an interpolated frame; and finally, an observation vector is updated by a time-variant gain Kalman filtering method so as to obtain a more accurate motion vector. Based on the method, a frame rate up-conversion motion estimation hardware architecture based on Kalman filtering is put forward. High utilization rate and high throughput of the system are realized through an alternate block scanning sequence and two parallel data channels.

Description

technical field [0001] The present invention relates to the technical field of video post-processing, in particular to a frame rate up-conversion motion estimation method and system based on Kalman filtering. Background technique [0002] LCD (Liquid Crystal Display) overcomes the shortcomings of CRT's bulky, power consumption and flickering, and has gradually replaced CRT in recent years as the most important display device in people's daily life. However, LCDs often produce shadows when displaying fast-moving videos, which affects the display effect of the video. From the perspective of signal processing, an effective solution is to use frame rate up-conversion technology, that is, to generate new frames in the middle of the original video source, thereby increasing the frame rate of the video source. Based on this, scholars have proposed a series of frame rate up-conversion methods, such as inserting black frames, frame repetition, and frame averaging. Although this met...

AI Technical Summary

Problems solved by technology

Therefore, most of the existing methods cannot meet the achievable requirements

The specific manifestations are: in the process of searching for motion vectors, too many points are searched, non-causal operations such as multiple iterations exist in the motion estimation process, and low bandwidth requirements are often ignored, making it difficult for existing methods to apply to the increasing resolution of input sources. Higher Video Processing Devices

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