Reduced Complexity Superresolution for Low-Bitrate Video Compression

Evolving video applications impose requirements for high image quality, low bitrate, and/or small computational cost. This paper combines state-of-the-art coding and superresolution (SR) techniques to improve video compression both in terms of coding efficiency and complexity. The proposed approach...

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Published in:	IEEE transactions on circuits and systems for video technology Vol. 26; no. 2; pp. 332 - 345
Main Authors:	Georgis, Georgios, Lentaris, George, Reisis, Dionysios
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Bit rate Codec Codecs Coding Complexity Complexity theory Computational efficiency Computing time Decoding Encoding high definition video Image coding Image compression Image quality Interpolation low-complexity codecs Noise levels Parameterization Parametrization PSNR Quality Reduction Rescaling Signal quality singleimage State of the art Super-resolution Video compression High-definition video single image superresolution (SR) video compression low-complexity codecs
ISSN:	1051-8215, 1558-2205
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Abstract	Evolving video applications impose requirements for high image quality, low bitrate, and/or small computational cost. This paper combines state-of-the-art coding and superresolution (SR) techniques to improve video compression both in terms of coding efficiency and complexity. The proposed approach improves a generic decimation-quantization compression scheme by introducing low complexity single-image SR techniques for rescaling the data at the decoder side and by jointly exploring/optimizing the downsampling/upsampling processes. The enhanced scheme achieves improvement of the quality and system's complexity compared with conventional codecs and can be easily modified to meet various diverse requirements, such as effectively supporting any off-the-shelf video codec, for instance H.264/Advanced Video Coding or High Efficiency Video Coding. Our approach builds on studying the generic scheme's parameterization with common rescaling techniques to achieve 2.4-dB peak signal-to-noise ratio (PSNR) quality improvement at low-bitrates compared with the conventional codecs and proposes a novel SR algorithm to advance the critical bitrate at the level of 10 Mb/s. The evaluation of the SR algorithm includes the comparison of its performance to other image rescaling solutions of the literature. The results show quality improvement by 5-dB PSNR over straightforward interpolation techniques and computational time reduction by three orders of magnitude when compared with the highly involved methods of the field. Therefore, our algorithm proves to be most suitable for use in reduced complexity downsampled compression schemes.
AbstractList	Evolving video applications impose requirements for high image quality, low bitrate, and/or small computational cost. This paper combines state-of-the-art coding and superresolution (SR) techniques to improve video compression both in terms of coding efficiency and complexity. The proposed approach improves a generic decimation-quantization compression scheme by introducing low complexity single-image SR techniques for rescaling the data at the decoder side and by jointly exploring/optimizing the downsampling/upsampling processes. The enhanced scheme achieves improvement of the quality and system's complexity compared with conventional codecs and can be easily modified to meet various diverse requirements, such as effectively supporting any off-the-shelf video codec, for instance H.264/Advanced Video Coding or High Efficiency Video Coding. Our approach builds on studying the generic scheme's parameterization with common rescaling techniques to achieve 2.4-dB peak signal-to-noise ratio (PSNR) quality improvement at low-bitrates compared with the conventional codecs and proposes a novel SR algorithm to advance the critical bitrate at the level of 10 Mb/s. The evaluation of the SR algorithm includes the comparison of its performance to other image rescaling solutions of the literature. The results show quality improvement by 5-dB PSNR over straightforward interpolation techniques and computational time reduction by three orders of magnitude when compared with the highly involved methods of the field. Therefore, our algorithm proves to be most suitable for use in reduced complexity downsampled compression schemes. Evolving video applications impose requirements for high image quality, low bitrate, and/or small computational cost. This paper combines state-of-the-art coding and superresolution (SR) techniques to improve video compression both in terms of coding efficiency and complexity. The proposed approach improves a generic decimation-quantization compression scheme by introducing low complexity single-image SR techniques for rescaling the data at the decoder side and by jointly exploring/optimizing the downsampling/upsampling processes. The enhanced scheme achieves improvement of the quality and syste's complexity compared with conventional codecs and can be easily modified to meet various diverse requirements, such as effectively supporting any off-the-shelf video codec, for instance H.264/Advanced Video Coding or High Efficiency Video Coding. Our approach builds on studying the generic scheme's parameterization with common rescaling techniques to achieve 2.4-dB peak signal-to-noise ratio (PSNR) quality improvement at low-bitrates compared with the conventional codecs and proposes a novel SR algorithm to advance the critical bitrate at the level of 10 Mb/s. The evaluation of the SR algorithm includes the comparison of its performance to other image rescaling solutions of the literature. The results show quality improvement by 5-dB PSNR over straightforward interpolation techniques and computational time reduction by three orders of magnitude when compared with the highly involved methods of the field. Therefore, our algorithm proves to be most suitable for use in reduced complexity downsampled compression schemes.
Author	Lentaris, George Georgis, Georgios Reisis, Dionysios
Author_xml	– sequence: 1 givenname: Georgios surname: Georgis fullname: Georgis, Georgios email: ggeorgis@phys.uoa.gr organization: Department of PhysicsElectronics Laboratory, National and Kapodistrian University of Athens, Athens, Greece – sequence: 2 givenname: George surname: Lentaris fullname: Lentaris, George email: glentaris@phys.uoa.gr organization: Department of PhysicsElectronics Laboratory, National and Kapodistrian University of Athens, Athens, Greece – sequence: 3 givenname: Dionysios surname: Reisis fullname: Reisis, Dionysios email: dreisis@phys.uoa.gr organization: Department of PhysicsElectronics Laboratory, National and Kapodistrian University of Athens, Athens, Greece
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SubjectTerms	Algorithms Bit rate Codec Codecs Coding Complexity Complexity theory Computational efficiency Computing time Decoding Encoding high definition video Image coding Image compression Image quality Interpolation low-complexity codecs Noise levels Parameterization Parametrization PSNR Quality Reduction Rescaling Signal quality singleimage State of the art Super-resolution Video compression
Title	Reduced Complexity Superresolution for Low-Bitrate Video Compression
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