No-reference pixel based video quality assessment for HEVC decoded video

Huang, Xin; Sgaard, Jacob; Forchhammer, Søren

doi:10.1016/j.jvcir.2017.01.002

“…By this technique, the image quality is increased in various ways. Huang et al [27] proposed a method, which adds high frequency information in videos frames. This technique did not perform well when noise ratio increased in the video frame.…”

Section: Literature Reviewmentioning

confidence: 99%

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Qureshi¹,

Nawaz²

2022

Preprint

0

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Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.

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“…To overcome such type of problems, many algorithms have been proposed in last two decades [15]- [18]. Huang et al [19] proposed a method, which adds high frequency information in videos frames. This technique did not perform well when noise ratio increased in the video frame.…”

Section: Literature Reviewmentioning

confidence: 99%

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Qureshi¹,

Nawaz²

2021

Preprint

0

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Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.

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“…Although this estimation of PSNR is not our final goal, it is used to build features provided to the ML mapping. Many papers 28,33,34 estimated PSNR without a reference signal (i.e., in an NR manner). Here, the procedure described in Ref.…”

Section: Feature Generation By Analysis Of H264mentioning

confidence: 99%

No-reference prediction of quality metrics for H.264-compressed infrared sequences for unmanned aerial vehicle applications

Hossain

¹

,

Mantel

²

,

Forchhammer

³

2019

Self Cite

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We propose a no-reference (NR) method for estimating the scores of metrics assessing the quality of infrared (IR) sequences compressed with H.264 for low-complexity unmanned aerial vehicle (UAV) applications. The scenario studied is to estimate the quality on an on-ground computer to avoid performing the processing onboard, due to the computational and memory limitations of the onboard hardware. For low complexity and fast feedback, a bitstream-based (BB) approach was chosen. The original IR sequences are captured by UAV, and then BB and pixel-based (PB) features are computed. Thereafter, a feature selection process is applied and the selected features are mapped using support vector regression, to predict the quality scores of full reference metrics. The method is evaluated for the NR prediction of four image and one video quality metrics. A set of five UAV-and three ground-IR sequences are used for evaluation. The proposed NR method consistently achieves robust results for the different objective metrics tested (Spearman rank-order correlation coefficients ranging from 0.91 to 0.99). A comparison with estimations based on features from three NR models from the literature proves to be in favor of the proposed method.

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No-reference pixel based video quality assessment for HEVC decoded video

Cited by 19 publications

References 32 publications

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

No-reference prediction of quality metrics for H.264-compressed infrared sequences for unmanned aerial vehicle applications

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