Variable Rate Video Compression using a Hybrid Recurrent Convolutional Learning Framework

• URL: http://arxiv.org/abs/2004.04244v2
• Date: Fri, 21 Aug 2020 20:21:24 GMT
• Title: Variable Rate Video Compression using a Hybrid Recurrent Convolutional Learning Framework
• Authors: Aishwarya Jadhav
• Abstract summary: This paper presents PredEncoder, a hybrid video compression framework based on the concept of predictive auto-encoding. A variable-rate block encoding scheme has been proposed in the paper that leads to remarkably high quality to bit-rate ratios.
• Score: 1.9290392443571382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, neural network-based image compression techniques have been able to outperform traditional codecs and have opened the gates for the development of learning-based video codecs. However, to take advantage of the high temporal correlation in videos, more sophisticated architectures need to be employed. This paper presents PredEncoder, a hybrid video compression framework based on the concept of predictive auto-encoding that models the temporal correlations between consecutive video frames using a prediction network which is then combined with a progressive encoder network to exploit the spatial redundancies. A variable-rate block encoding scheme has been proposed in the paper that leads to remarkably high quality to bit-rate ratios. By joint training and fine-tuning of this hybrid architecture, PredEncoder has been able to gain significant improvement over the MPEG-4 codec and has achieved bit-rate savings over the H.264 codec in the low to medium bit-rate range for HD videos and comparable results over most bit-rates for non-HD videos. This paper serves to demonstrate how neural architectures can be leveraged to perform at par with the highly optimized traditional methodologies in the video compression domain.

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