  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.
