Method suitable for compressing sequence images of unmanned aerial vehicle

Abstract

The invention discloses a method suitable for compressing sequence images of an unmanned aerial vehicle. The method comprises the following steps: according to airborne auxiliary data, carrying out intelligent dynamic coding frame group division of the sequence images of the unmanned aerial vehicle; within a frame group, carrying out high-precision quick motion estimation of the first frame and the last frame to the current frame, thereby acquiring a number pair matching point; according to corresponding matching relation, obtaining projection transformation matrixes of the first frame and the last frame to the current frame so as to acquire a forecast image and residual error; combining residual error images of the first frame and the other frames of the same frame group into a singe image; and compressing the singe image by JPEG2000 to acquire a high-performance compression result by a global optimization pruning algorithm. The method takes into account the characteristics of the sequence images of the unmanned aerial vehicle and makes full use of the characteristics of airborne auxiliary information and the JPEG2000 compression algorithm; therefore, the method has the characteristics of low algorithm complexity, good compression performance, strictly controlled compression code rate, easily realized hardware, and the like.

Description

technical field [0001] The invention relates to image data processing technology, in particular to a compression method suitable for sequence images of drones. Background technique [0002] With the development of imaging technology, the resolution of images acquired by drones continues to increase, and the amount of image data increases sharply. However, the bandwidth of communication channels is very limited. Therefore, it is necessary to effectively compress the acquired image data in real time to greatly reduce its data volume. On the other hand, UAVs fly fast and fly at a high altitude. Compared with ordinary images, UAV images have higher resolution, poorer spatial correlation, and significantly different temporal correlations from ordinary videos. Existing image and video compression algorithms and systems are more or less deficient in algorithm complexity, compression performance, compression speed, response time, code flow control, etc., and cannot fully meet the n...

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On the other hand, UAVs fly fast and fly at a high altitude. Compared with ordinary images, UAV images have high resolution and poor spatial correlation. The temporal correlation is also significantly different from ordinary video.

Existing image and video compression algorithms and systems are more or less deficient in algorithm complexity, compression performance, compression speed, response time, code flow control, etc., and cannot fully meet the needs of engineering practice, such as the currently used MJPEG2000 algorithm (Reference: ISO/IEC 15444-3Motion-JPEG2000 (JPEG2000Part 3), 2002.), which uses the JPEG2000 algorithm for int

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