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Inertial data compression method, server and computer readable storage medium

A data compression and server technology, applied in the field of human-computer interaction, can solve the problems of low compression rate, large delay in inertial data processing, and large data loss, and achieve the effect of improving compression rate, improving prediction effect, and reducing compression loss.

Active Publication Date: 2018-05-01
SHENZHEN TCL NEW-TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is an inertial data compression method, server and computer-readable storage medium, aiming to solve the problems of large time delay, low compression rate and large data loss in inertial data processing in the prior art

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Embodiment Construction

[0041] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0042] The solution of the embodiment of the present invention is mainly as follows: the present invention obtains the inertial data to be compressed in the inertial sensor through the server, and extracts the features of the inertial data to be compressed through the preset principal component mapping model to obtain the mapping feature value. Set the principal component mapping model to reflect the mapping relationship between inertial data and eigenvalues, perform linear predictive encoding on the mapped eigenvalues, obtain predictive encoding results, perform compression encoding on the predictive encoding results, and use the preset principal component mapping model to be compressed The feature extraction of inertial data can improve the prediction effect of predictive coding results, and the compression coding of...

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Abstract

The invention discloses an inertial data compression method, a server and a computer readable storage medium. The method includes the steps: acquiring inertial data to be compressed in an inertial sensor by the aid of the server; performing feature extraction on the inertial data to be compressed by the aid of a preset main component mapping model to obtain a mapping feature value; reflecting a mapping relationship between the inertial data and the feature value by the preset main component mapping model; performing linear predictive coding on the mapping feature value to obtain a predictive coding result; performing compression coding on the predictive coding result. Features of the inertial data to be compressed are extracted by the preset main component mapping model, predictive effectsof the predictive coding result can be improved, the predictive coding result is compressed and encoded, processing delay of the inertial data can be reduced, compression ratio is improved, processing and calculating complexity of the inertial data is reduced, and compression loss of the inertial data is reduced.

Description

technical field [0001] The invention relates to the field of human-computer interaction, in particular to an inertial data compression method, a server and a computer-readable storage medium. Background technique [0002] Human-computer interaction, as a new way of interpersonal communication, is an important research direction in the fields of multimedia, industrial control, and visual interaction. According to the different carriers of human-computer interaction, it can be roughly divided into the following categories: image-based human-computer interaction, voice signal-based human-computer interaction, and sensor-based human-computer interaction. Among them, sensor-based tracking of human-computer interaction, one of the important directions is based on inertial sensor (inertial sensor) human-computer interaction. With the rapid popularization of Android smart TVs, the demand for various human-computer interaction methods based on inertial sensors is also increasing. A...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
CPCH03M7/3071Y02D30/70
Inventor 吴佳飞赖长明
Owner SHENZHEN TCL NEW-TECH CO LTD
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