A Computational Complexity Aware Macroblock-Level Parallel Scheduling Method for Video Decoding

Abstract

The invention discloses a video decoding macro-block-grade parallel scheduling method for perceiving calculation complexity. The method comprises two critical technologies: the first one involves establishing a macro-block decoding complexity prediction linear model according to entropy decoding and macro-block information after reordering such as the number of non-zero coefficients, macro-block interframe predictive coding types, motion vectors and the like, performing complexity analysis on each module, and fully utilizing known macro-block information so as to improve the parallel efficiency; and the second one involves combining macro-block decoding complexity with calculation parallel under the condition that macro-block decoding dependence is satisfied, performing packet parallel execution on macro-blocks according to an ordering result, dynamically determining the packet size according to the calculation capability of a GPU, and dynamically determining the packet number according to the number of macro-blocks which are currently parallel so that the emission frequency of core functions is also controlled while full utilization of the GPU is guaranteed and high-efficiency parallel is realized. Besides, parallel cooperative operation of a CPU and the GPU is realized by use of a buffer area mode, resources are fully utilized, and idle waiting is reduced.

Description

technical field [0001] The present invention belongs to the technical field of video decoding, and more specifically relates to a computational complexity-aware macroblock-level parallel scheduling method for video decoding. The estimation result of stage computational complexity schedules the parallel execution sequence of macroblock decoding in the video frame, reduces the synchronization overhead of parallel processing, achieves the effect of accelerating video decoding and saving system energy consumption. Background technique [0002] At present, in applications such as digital TV, mobile video, and video surveillance, full high-definition 1080P digital video has become very popular, and video resolution is developing toward ultra-high-definition, such as 4K and 8K. The improvement of video resolution can provide a better user experience, but it also increases the computational complexity of the encoding and decoding process dramatically, posing a huge challenge to its ...

AI Technical Summary

Problems solved by technology

Because the number of CPU cores is relatively small, the number of parallel threads is limited, and the existing ordinary GPU generally has thousands of stream processors. The number of threads in the CPU-based scheduling scheme is kept at the same level as the number of CPU cores. Quantitative level, so can not take full advantage of the many stream processors that the GPU has

In addition, the parallel scheduling scheme implemented by queues and thread pools requires a lot of logical judgments, and GPU is more suitable for arithmetic operations, not suitable for this type of scheduling

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