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JBE, vol. 26, no. 4, pp.441-452, July, 2021
Luma Mapping Function Generation Method Using Attention Map of Convolutional Neural Network in Versatile Video Coding Encoder
Naseong Kwon, Jongseok Lee, Joohyung Byeon, and Donggyu Sim
C.A E-mail: email@example.com
In this paper, we propose a method for generating luma signal mapping function to improve the coding efficiency of luma signal mapping methods in LMCS. In this paper, we propose a method to reflect the cognitive and perceptual features by multiplying the attention map of convolutional neural networks on local spatial variance used to reflect local features in the existing LMCS. To evaluate the performance of the proposed method, BD-rate is compared with VTM-12.0 using classes A1, A2, B, C and D of MPEG standard test sequences under AI (All Intra) conditions. As a result of experiments, the proposed method in this paper shows improvement in performance the average of -0.07% for luma components in terms of BD-rate performance compared to VTM-12.0 and encoding/decoding time is almost the same.
Keyword: VVC, Encoder, Luma mapping with Chroma Scaling, CNN
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