Objective quality analysis of MPEG-1, MPEG-2 &amp; Windows Media video

Aeluri, Praveen K.; Bojan, Vinodh; Richie, S.M.; Weeks, Arthur R.

doi:10.1109/iai.2004.1300978

Cited by 7 publications

(5 citation statements)

References 1 publication

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“…Therefore, there are a multitude of alternative video coding standards and formats published by entities ranging from major industry players to industry consortiums that are proprietary for specific applications or are royalty free for industrial use. AVS [15], WMV [16], Theora [17], VP8 [18], VP9 [19], and AV1 [20] are all such video compression formats that have a significant market presence.…”

Section: A Conventional Video Codingmentioning

confidence: 99%

A Semantic Communication and VVC Based Hybrid Video Coding System

Samarathunga,

Ganearachchi,

Fernando

et al. 2024

IEEE Access

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Requirements of next-generation video applications are becoming a challenge for conventional video coding systems, although they have evolved over decades to accommodate the most demanding of current video applications. Semantic communications, built on the concept of transmitting just the semantics of a message and allowing the receiver to reconstruct the message based on a shared context, is a non-conventional approach being considered to overcome these challenges and improve performance of video coding systems. In this paper, a first such semantic communication-based video coding system in hybrid mode is proposed, which uses an autoencoder-based semantic encoder for inter coding, augmented by the intra coding capabilities of Versatile Video Coding (VVC) to encode key frames that form the context for the semantic communication and the residuals for improving the fidelity of the output frames. For a range of videos with differing levels of complexity, the proposed system consistently outperforms High Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC) in terms of rate distortion metrics quantified by Bjontegaard Delta Rates. It also outperforms Versatile Video Coding with videos with low or high complexity, but slightly falls behind with videos with medium complexity, which can be improved by addressing the open research areas that stem from this work. The proposed system demonstrates the potential of semantic communication based video coding systems to consistently outperform state-of-theart conventional video coding systems over a wide range video applications.

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Section: A Conventional Video Codingmentioning

confidence: 99%

A Semantic Communication and VVC Based Hybrid Video Coding System

Samarathunga,

Ganearachchi,

Fernando

et al. 2024

IEEE Access

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“…However, the acquired process of related information in coding is too complicated. Aeluri et al (2004) combine the four parameters: motion characteristic, encoder, frame rate, and bit rate based on the MSE to assess the video quality. The ANSI National Telecommunications and Information Administration (NTIA) General Model for measuring video quality adopts seven parameters including the loss of spatial information, the shift of edges from horizontal and vertical to diagonal, the shift of edges from diagonal to horizontal and vertical, the spread of the distribution of two-dimensional color samples, the quality improvement from edge sharpening or enhancements, the interactive effects from spatial and temporal, and the impairments from the extreme chroma.…”

Section: Related Workmentioning

confidence: 99%

The difference between perceived video quality and objective video quality

Sun

Huang

2009

J Vis

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We select 93 video sequences encoded/decoded by Microsoft MPEG-4 software to classify them into six different content characteristics by the cluster analysis and the discriminant analysis in this study. We compare the peak signal noise ratio (PSNR) of objective video quality evaluation with the mean opinion score of subjective quality evaluation to understand their difference by varying different bit rate. We find that the acceptable satisfaction of user perceived quality for different motion and texture characteristics is significantly different in varying the bit rates. For the low motion and simple texture characteristic (Type 1) and the low motion and complex texture characteristic (Type 4), when the bit rate is 2,000 kbps, the tolerable discarded ratio is allowed to 44% and 49%, and the PSNR is 41.7 dB and 41.0 dB, subjects could perceive the acceptable satisfaction of video quality. For the middle motion and simple texture characteristic (Type 2) and the high motion and simple texture characteristic (Type 3), when the bit rate needs around 3,000 kbps, the tolerable discarded ratio must be controlled below 12% and 34%, and the PSNR is 45.2 dB and 41.7 dB, subjects could perceive the acceptable satisfaction of video quality. For the middle motion and complex texture characteristic (Type 5) and the high motion and complex texture characteristic (Type 6), when the bit rate is 6,000 kbps, the tolerable discarded ratio is allowed to 14%, and the PSNR is 43.2 dB and 42.9 dB, subjects could perceive the acceptable satisfaction of video quality.

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“…6, where v 4 and v 5 are plotted against the average SAD/pixel. An estimation of v 4 and v 5 as a function of the average SAD/pixel can be performed, as presented in (7): v 4 = 0.208s 0.95 + 0.036, v 5 = 0.036s 1.52 + 1.17 (7) where s is the average video clip SAD/pixel (i.e. SAD/64 for 8 × 8 blocks).…”

Section: Existing Video Quality Modelsmentioning

confidence: 99%

“…Several comparisons and characterizations of different codecs have been made, contrasting the image quality obtained regarding to the original image (FR models), or predicting the image quality based only in the degraded image (NR) [4][5][6][7][8][9]. However, most of these works have been made using non-perceptual metrics such as PSNR.…”

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confidence: 99%

A parametric model for perceptual video quality estimation

Joskowicz

Ardao

2010

Telecommun Syst

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In this paper, a parametric model is proposed which provides estimation for the perceived quality of video, coded with different codecs, at any bit rate and display format. The video quality metric used is one of the standardized Full Reference models in Recommendations ITU-T J.144 and ITU-R BT.1683. The proposed model is based on the video quality estimation described in Recommendation ITU-T G.1070, but incorporates different enhancements, allowing a much better estimation of the perceptual MOS values, especially in low bit rate ranges. The error obtained with the proposed model, regarding to the ITU models, is between the ITU algorithms error margins, according to the subjective tests developed by the VQEG. Studies were made for more than 1500 processed video clips, coded in MPEG-2 and H.264/AVC, in bit rate ranges from 50 kb/s to 12 Mb/s, in SD, VGA, CIF and QCIF display formats.

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Objective quality analysis of MPEG-1, MPEG-2 & Windows Media video

Cited by 7 publications

References 1 publication

A Semantic Communication and VVC Based Hybrid Video Coding System

A Semantic Communication and VVC Based Hybrid Video Coding System

The difference between perceived video quality and objective video quality

A parametric model for perceptual video quality estimation

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