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arXiv · 2010.05760

Video Quality Enhancement Using Deep Learning-Based Prediction Models for Quantized DCT Coefficients in MPEG I-frames

Antonio J G Busson·Paulo R C Mendes·Daniel de S Moraes·Álvaro M da Veiga·Álan L V Guedes·Sérgio Colcher
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Abstract

Recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG/MPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a MPEG video decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCT coefficients received from a low-quality I-frames bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same frames with enhanced quality. In experiments with a video dataset, our best model was able to improve from frames with quantized DCT coefficients corresponding to a Quality Factor (QF) of 10 to enhanced quality frames with QF slightly near to 20.

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Antonio J G Busson, Paulo R C Mendes, Daniel de S Moraes, Álvaro M da Veiga, Álan L V Guedes, Sérgio Colcher. 2020-10-09. Video Quality Enhancement Using Deep Learning-Based Prediction Models for Quantized DCT Coefficients in MPEG I-frames. https://doi.org/10.1109/ism.2020.00012

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