Reference frame data compression method and apparatus

Abstract

The invention relates to the field of image processing and discloses a reference frame data compression method and apparatus. The compression method comprises the following steps: segmenting a reference frame into multiple data blocks with predetermined sizes; for each data block, calculating compression rates when various candidate prediction modes are applied, and selecting a prediction mode with a highest compression rate for compressing the data blocks, wherein each candidate prediction mode only employs each pixel point in the corresponding data block for in-block prediction, each candidate prediction mode segments the data blocks into multiple groups, each group comprises multiple pixel points, and each candidate prediction mode comprises a prediction mode in which the pixel points in the groups are distributed according to a 45-degree or 135-degree direction. According to the invention, the prediction mode corresponding to the highest compression rate can be selected for individually compressing the data blocks, such that the compression rate and the compression success rate of the reference frame are effectively improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a reference frame data compression technology. Background technique [0002] With the continuous development of video coding standards, various coding standards, such as H.264 (a video codec standard), H.265 (a video coding standard), VP9 (a video compression standard), etc., all The size of the supported encoded pictures is getting larger and larger, and high-definition encoding and 4K encoding have become commonplace. The increase in image size directly leads to a substantial increase in reference frame data, and the read / write bandwidth of the encoder chip also increases accordingly. In some encoder systems with tight bandwidth, the read / write bandwidth will have a direct impact on the performance of the encoder. [0003] At present, reference frame compression methods can be simply divided into two categories: one is lossy compression, and the other is lossless compression. ...

AI Technical Summary

Problems solved by technology

Compared with lossless compression, lossy compression has a better compression rate, but this will reduce the quality of the encoded image. For this reason, most encoders use lossless compression or a combination of lossy and lossless compression.

However, the current compression methods still have problems such as low compression rate, inflexible encoding, high transport complexity, and complex encoding. Therefore, it is very important for the encoder to develop a reference frame compression method with a high compression rate.

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