Efficient regression test method based on hash eigen value detection of audio video decoder

Abstract

The invention discloses an efficient regression test method based on hash eigen value detection of audio video decoder. The efficient regression test method is realized through the following steps: generating unique and specific hash eigen value from decoded data; and verifying the accuracy of decoding after version upgrading by comparing the hash eigen value of the previous version to that of the current version. On the one hand, it avoids the inaccuracy from omissions caused by subjective factors such as human senses, and on the other hand, all the method needs to do is turn to a comparing tool to compare the files in the previous version and the current version with hash eigen value. This eliminates the procedures and time to play and watch the materials and raises the efficiency greatly. In addition, as the hash eigen value decoded by a standard decoder is unique and accurate, so as long as the contents decoded by the decoder of a new version are consistent with those by the standard decoder, then, that result is deemed to be accurate.

Description

technical field [0001] The invention relates to an efficient regression testing method based on hash feature value detection of audio and video decoders. Background technique [0002] There are many types of audio and video decoding involved in the current multimedia field. In many cases, after each audio and video decoder version is upgraded, the test for the decoding module needs to cover a large number of test materials each time, such as H.264, H265, Mainstream encoding formats such as AAC and MPEG2, and different encoding formats have different parameters such as resolution, aspect ratio, encoding grade, and encoding level, which lead to a large test material library, and each time the decoder is upgraded It is necessary to repeatedly traverse this huge material library for verification, resulting in a very low verification efficiency. In addition, the currently commonly used testing methods are all manually verified. The verification process is first to decode the mat...

AI Technical Summary

Problems solved by technology

[0002] There are many types of audio and video decoding involved in the current multimedia field. In many cases, after each audio and video decoder version is upgraded, the test for the decoding module needs to cover a large number of test materials each time, such as H.264, H265, Mainstream encoding formats such as AAC and MPEG2, and different encoding formats have different parameters such as resolution, aspect ratio, encoding grade, and encoding level, etc., resulting in a large test material library, and after each decoder upgrade It is necessary to repeatedly traverse this huge material library for verification, resulting in a very low verification efficiency

In addition, the currently commonly used testing methods are all manually verified. The verification process is first to decode the materials in the material library with a decoder and then use the renderer to play, and observe the images and sounds through the human body. Aspects, such as fatigue, lack of concentration and other reasons, miss decoding wrong images or sounds, resulting in reduced accuracy

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