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<p>In this thesis, two aspects of video coding improvement are discussed, namely
error resilience and coding efficiency.
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<p>With the increasing amount of videos being created and consumed, better video
compression tools are needed to provide reliable and fast transmission. Many popular
video coding standards such as VPx, H.26x achieve video compression by using spa-
tial and temporal dependencies in the source video signal. This makes the encoded
bitstream vulnerable to errors during transmission. In this thesis, we investigate an
error resilient video coding for the VP9 bitstreams using error resilience packets. An
error resilient packet consists of encoded keyframe contents and the prediction sig-
nals for each non-keyframe. Experimental results exhibit that our proposed method
is effective under typical packet loss conditions.
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<p>In the second part of the thesis, we first present an automatic stillness feature
detection method for group of pictures. The encoder adaptively chooses the coding
structure for each group of pictures based on its stillness feature to optimize the
coding efficiency.
</p>
<p>Secondly, a content-based video coding method is proposed. Modern video codecs
including the newly developed AOM/AV1 utilize hybrid coding techniques to remove
spatial and temporal redundancy. However, the efficient exploitation of statistical
dependencies measured by a mean squared error (MSE) does not always produce the
best psychovisual result. One interesting approach is to only encode visually relevant
information and use a different coding method for “perceptually insignificant” regions
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<p>xiv
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<p>in the frame. In this thesis, we introduce a texture analyzer before encoding the input
sequences to identify detail irrelevant texture regions in the frame using convolutional
neural networks. The texture region is then reconstructed based on one set of motion
parameters. We show that for many standard test sets, the proposed method achieved
significant data rate reductions.
</p>
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| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12798140 |
| Date | 13 August 2020 |
| Creators | Di Chen (9234905) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Relation | https://figshare.com/articles/thesis/Advancing_Video_Compression_With_Error_Resilience_And_Content_Analysis/12798140 |
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