32 research outputs found
Encoding in the Dark Grand Challenge:An Overview
A big part of the video content we consume from video providers consists of
genres featuring low-light aesthetics. Low light sequences have special
characteristics, such as spatio-temporal varying acquisition noise and light
flickering, that make the encoding process challenging. To deal with the
spatio-temporal incoherent noise, higher bitrates are used to achieve high
objective quality. Additionally, the quality assessment metrics and methods
have not been designed, trained or tested for this type of content. This has
inspired us to trigger research in that area and propose a Grand Challenge on
encoding low-light video sequences. In this paper, we present an overview of
the proposed challenge, and test state-of-the-art methods that will be part of
the benchmark methods at the stage of the participants' deliverable assessment.
From this exploration, our results show that VVC already achieves a high
performance compared to simply denoising the video source prior to encoding.
Moreover, the quality of the video streams can be further improved by employing
a post-processing image enhancement method
Deep learning-based switchable network for in-loop filtering in high efficiency video coding
The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering
Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction
In VP9 video codec, the sizes of blocks are decided during encoding by
recursively partitioning 6464 superblocks using rate-distortion
optimization (RDO). This process is computationally intensive because of the
combinatorial search space of possible partitions of a superblock. Here, we
propose a deep learning based alternative framework to predict the intra-mode
superblock partitions in the form of a four-level partition tree, using a
hierarchical fully convolutional network (H-FCN). We created a large database
of VP9 superblocks and the corresponding partitions to train an H-FCN model,
which was subsequently integrated with the VP9 encoder to reduce the intra-mode
encoding time. The experimental results establish that our approach speeds up
intra-mode encoding by 69.7% on average, at the expense of a 1.71% increase in
the Bjontegaard-Delta bitrate (BD-rate). While VP9 provides several built-in
speed levels which are designed to provide faster encoding at the expense of
decreased rate-distortion performance, we find that our model is able to
outperform the fastest recommended speed level of the reference VP9 encoder for
the good quality intra encoding configuration, in terms of both speedup and
BD-rate