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Real-time video super-resolution processing method integrated with cortex-A7

A super-resolution, real-time video technology, applied in image data processing, instruments, calculations, etc., can solve the problems of low processing quality, immature and limited real-time video streams, etc.

Inactive Publication Date: 2014-12-10
BEIJING INST OF TECH ZHUHAI CAMPUS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the number of low-resolution images is limited, the super-resolution algorithm based on the Gaussian Markov random field model tends to lose important details and has a high distortion rate.
In addition, existing current super-resolution algorithms are still limited to processing single images, and the technology for processing real-time video streams is not yet mature.
If the existing super-resolution algorithm is still used to process real-time video streams, it will result in slower processing speed, higher processing cost and lower processing quality

Method used

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  • Real-time video super-resolution processing method integrated with cortex-A7
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Embodiment 1

[0131] In order to save hardware cost and labor cost, the present invention implements the function of real-time video super-resolution through a cheap and high-quality SOC system on chip, so as to meet the requirements of enterprises and related institutions for high-resolution imaging.

[0132] The SOC system on chip of the present invention is composed of two parts, a hardware module and a software module. Among them, the main components of the hardware part include: a. High-efficiency processing chip based on ARMv7-A architecture; b. DDR3 processor and its external devices (camera, mouse, keyboard, display, power supply); c. SOC chip external interface ( Including high-speed SDRAM data interface, video data input interface, video data output interface, interrupt interface, DDR3 data interface). The entire hardware module structure is as follows Figure 8 shown.

[0133] Figure 8 Among them, 1 is the main control processor of the SOC-based system on a chip, 2 is the dat...

Embodiment 2

[0138] The present invention integrates the real-time video super-resolution processing method of Cortex-A7, closely combines traditional super-resolution processing technology, traditional video coding and decoding technology and traditional image coding technology, researches and proposes a new method based on software and hardware platforms improve algorithm. In the improved algorithm encoding algorithm of real-time video super-resolution integrated with Cortex-A7, to ensure that it can run smoothly and achieve the best effect on the hardware platform SOC system on chip, the key lies in the objective quality of video encoding and decoding technology and its processing video image algorithm technology . Because the codec algorithm is convincing only if the bit rate is reduced under the premise of the same decoded image peak signal-to-noise ratio (PSNR). One of the important factors affecting the objective quality of the decoded video image is the theoretical quality of the ...

Embodiment 3

[0140] The OpenCV function library has a variety of interpolation functions and an integration function based on a variational algorithm, but the experimental results are not ideal. The recent mainstream dictionary learning and sparse representation super-resolution techniques are not available in the OpenCV library. Therefore, under the demands of market economy and the needs of technological development, the present invention proposes an improved new algorithm, which is embedded in the OpenCV function library, to achieve portable, real-time, real-time video super-resolution processing of SOC system-on-chip need.

[0141] This embodiment describes in detail the new video encoding and decoding through the SOC system of the present invention and the super-resolution algorithm combined with the improved clustering dictionary learning and feature sparse representation.

[0142]The improved clustering dictionary learning and feature sparse representation super-resolution algorith...

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Abstract

The invention discloses a real-time video super-resolution processing method integrated with cortex-A7. The method includes that (1), video sampling is performed, and a low-resolution video frame is obtained and input to a system on chip (SOC); (2) the low-resolution video frame is sequentially subjected to complexity processing, feature vector extracting and sample set training, feature vectors to be matched are obtained, and a sample set is established by high-resolution high-frequency components; (3) according to the feature vectors to be matched, the low-resolution video frame is subjected to super-resolution processing by means of an improved super-resolution algorithm based on cluster dictionary self-learning and feature sparse representation and combination with SOC coding and decoding technologies, and thereby, a high-resolution video frame flow is output. The real-time video super-resolution processing method integrated with cortex-A7 has the advantages of real time, low distortion rate and processing costs and high processing speed and qualities, and the method can be widely applied to the field of video image processing.

Description

technical field [0001] The invention relates to the field of video image processing, in particular to a real-time video super-resolution processing method integrated with Cortex-A7. Background technique [0002] At present, for most imaging devices, the resolution of images acquired is still very low, and replacing devices requires a lot of material and manpower input. At the same time, in the video image acquisition, due to the influence of various factors such as the accuracy of the imaging equipment or the distance between the equipment and the target, the movement of the target, and noise, the obtained video images are usually noisy, blurred, and relatively low-resolution. It is difficult to obtain an ideal resolution image. Limited image resolution will affect the performance of the system, such as low-resolution images will reduce the recognition performance of the system. This often brings difficulties to work such as target recognition, identification or criminal i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T9/00
Inventor 苏秉华唐佳林庄广利
Owner BEIJING INST OF TECH ZHUHAI CAMPUS
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