Research and application of improved neural network optimization algorithm

Sun, Weixiang

doi:10.1117/12.2660936

Cited by 1 publication

(1 citation statement)

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“…With the rapid development of machine learning and deep learning technologies, differential evolution algorithms have also been applied to artificial intelligence (AI) methods. Sun [46] proposes a novel personalized recommendation algorithm for learning resources based on DE and graph neural networks (GNN). The differential evolution algorithm is utilized to optimize model hyperparameters, resulting in improved recommendation performance.…”

Section: Introductionmentioning

confidence: 99%

Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding

Jin,

Chai

2023

Applied Sciences

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High-Efficiency Video Coding (HEVC) is one of the most widely studied coding standards. It still uses the block-based hybrid coding framework of Advanced Video Coding (AVC), and compared to AVC, it can double the compression ratio while maintaining the same quality of reconstructed video. The quantization module is an important module in video coding. In the process of quantization, quantization parameter is an important factor in determining the bitrate in video coding, especially in the case of limited channel bandwidth. It is particularly important to select a reasonable quantization parameter to make the bitrate as close as possible to the target bitrate. Aiming at the problem of unreasonable selection of quantization parameters in codecs, this paper proposes using a differential evolution algorithm to assign quantization parameter values to the coding tree unit (CTU) in each frame of 360-degree panoramic video based on HEVC so as to strike a balance between bitrate and distortion. Firstly, the number of CTU rows in a 360-degree panoramic video frame is considered as the dimension of the optimization problem. Then, a trial vector is obtained by randomly selecting the vectors in the population for mutation and crossover. In the mutation step, the algorithm generates a new parameter vector by adding the weighted difference between two population vectors to a third vector. And the elements in the new parameter vector are selected according to the crossover rate. Finally, the trial vector is regarded as the quantization parameter of each CTU in the CTU row to encode, and the vector with the least rate distortion is selected. The algorithm will produce the optimal quantization parameter combination for the current video. The experimental results show that compared to the benchmark algorithm of HEVC reference software HM-16.20, the proposed algorithm can provide a bit saving of 1.86%, while the peak signal-to-noise ratio (PSNR) can be improved by 0.07 dB.

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Section: Introductionmentioning

confidence: 99%