Block partitioning coding complexity optimization method and device based on deep learning method
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A coding complexity and deep learning technology, applied in the field of block segmentation coding complexity optimization, can solve the problems of inability to model the CU segmentation process, limited learning ability, and shallow CNN structure.
Active Publication Date: 2018-09-04
BEIHANG UNIV
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However, CNNs in the prior art have a shallow structure and limited learning ability, so they cannot accurately model the complex CU segmentation process
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Embodiment 1
[0091] Such as Figure 6 As shown, the block segmentation coding complexity optimization method based on deep learning method of the present invention may comprise the following steps:
[0092] 101. In HEVC, check the frame coding mode currently used by the HEVC;
[0093] 102. Select a CU partition prediction model corresponding to the frame coding mode according to the frame coding mode; the CU partition prediction model is a pre-established and trained model with an early termination mechanism.
[0094] 103. Predict a CU partition result in the HEVC according to the selected CU partition prediction model, and partition the entire coding tree unit CTU according to the predicted CU partition result.
[0095] Specifically, after executing the above Figure 6 Before the shown method, the above-mentioned method may also include step 600 not shown in the following figures:
[0096] Build the ETH-CNN, train the ETH-CNN; build the ETH-LSTM, train the ETH-LSTM.
[0097] Wherein, ...
Embodiment 2
[0197] In addition, the block segmentation coding complexity optimization method based on the deep learning method in the embodiment of the present invention described above may be implemented by a block partition coding complexity optimization device. Such as Figure 7 shown.
[0198] The device for optimizing complexity of block segmentation coding based on deep learning methods may include a processor 501 and a memory 502 storing computer program instructions.
[0199] Specifically, the above-mentioned processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits in the embodiments of the present invention.
[0200] Memory 502 may include mass storage for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (Hard Disk Drive, HDD), a floppy disk drive, a flash memory...
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Abstract
The invention provides a block partitioning coding complexity optimization method and device based on a deep learning method. The method comprises the following steps: checking a frame coding mode currently used by HEVC (high efficiency video coding) in the HEVC; selecting a CU partition prediction model corresponding to the frame coding mode according to the frame coding mode, wherein the CU partition prediction model is a model established and trained in advance; predicting a CU partition result in the HEVC according to the selected CU partition prediction model, and partitioning the whole CTU according to a predicted CU partition result. In the specific application, the frame coding mode is an intra-frame mode, and the CU partition prediction model is the ETH-CNN capable of being terminated in advance; if the frame coding mode is an inter-frame mode, the CU partition prediction model can the ETH-LSTM and ETH-CNN capable of being terminated in advance. Through the above method provided by the invention, the time required for decoding the CU partition in the coding is obviously shortened in the premise of guaranteeing the CU partition prediction precision, and the HEVC coding complexity is effectively reduced.
Description
technical field [0001] The present invention relates to the technical field of video coding, in particular to a block segmentation coding complexity optimization method and device based on a deep learning method. Background technique [0002] Compared with the previous generation H.264 / Advanced Video Coding (AVC) standard, the High Efficiency Video Coding (HEVC) standard can save about 50% of the bit rate under the same video quality. This benefits from some advanced video coding techniques, such as the quadtree-based coding unit (coding unit, CU) partition structure. However, these techniques also bring considerable complexity. The encoding time of state-of-the-art HEVC is about 253% longer than H.264 / AVC on average, which limits the practical application of this standard. Therefore, it is necessary to significantly reduce the complexity of HEVC coding under the premise that the rate-distortion (RD) performance is hardly affected. [0003] Over the past few years, variou...
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