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

--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. The application of methods based on Deep Learning (DL) in multimedia systems has opened a range of cognitive features in many directions that go beyond the traditional functionalities of capturing, streaming and presenting information. It has provided a whole new extent of capabilities that includes detection and classification of objects. New platforms and development techniques were tailored, and entirely new frameworks were brought together to enhance the development of such systems [1] trying to fill in the gap between this vast (and relatively new) technological knowledge and the practical development of modern systems.