Video lossless compression method based on adaptive template

Abstract

The invention discloses a video lossless compression method based on adaptive model selection. The method comprises the following steps: judging the type of an input video, determining operand, performing video pre-processing operation; adopting the airspace redundancy-removing method, the time domain redundancy-removing method and the direct mode redundancy-removing method to perform redundancy-removing operation to the current sample and obtain the predictive value of the current sample; utilizing the redundant information of space, time and frequency domain to separately perform infraframe prediction and interframe prediction to the original YUV sequence; if the original sequence is RGB sequence, performing infraframe prediction, interframe prediction and direct prediction to the converted sequence; using an adaptive prediction model selector to judge the predictive value of the current sample and determine that the minimum is the optimal prediction error; and finally performing context-based arithmetic encoding to the map value of the final prediction error and outputting to obtain bit stream. The invention uses the adaptive prediction model selector to replace the additional bit prediction model, thus obviously increasing the compression efficiency of video; and the method can be used for video compression in the fields of aviation and navigation.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to a video lossless compression method, which can be used in aviation and navigation fields with higher resolution requirements. Background technique [0002] In recent years, digital image and video compression has made great progress, and advanced compression standards such as MPEG-4, JPEG2000, H.264 / MPEG-4AVC have appeared successively. Most of these standards focus on lossy compression, but in many practical applications, lossless digital image and video compression is more important. For example, massive aerial and remote sensing images must be compressed to save storage space and improve transmission efficiency. However, if lossy compression is used, images with compressed detail information will miss important target information, resulting in errors. Therefore, the research on lossless digital image and video compression has gradually gained the attention of s...

AI Technical Summary

Problems solved by technology

Elias' method uses a new time-domain predictor, which reduces the algorithm complexity, but also reduces the performance of the prediction

In 2001, G.C.K. analyzed the wavelet transform of the residual image after motion compensation, and found that it cannot effectively reduce the entropy, and it cannot be effectively compressed. G CK Abhayaratne.D M Monco.″Embedded to Loeeless Coding of Compensated Prediction Residuals inLoseless Video Coding", Proc of SPIE, 2001, 4310, pp: 75-85

Although the above-mentioned algorithms proposed in recent years have improved in terms of performance, their algorithm complexity is high, and the selection of adaptive models is still not very effective, resulting in low video compression efficiency.

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