Constrained and general dynamic rate shaping of compressed digital video

Eleftheriadis, Alexandros; Anastassiou, Dimitris

doi:10.1109/icip.1995.537655

Cited by 78 publications

(44 citation statements)

References 7 publications

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“…One of the first suggested methods, presented by Eleftheriadis and Anastassiou [Eleftheriadis and Anastassiou 1995], uses a rate-distortion function to find minimal distortion. Based on the bandwidth capacity predicted via monitoring the current state of the network, the video is dynamically reshaped with different quantization values.…”

Section: Related Workmentioning

confidence: 99%

Video quality for face detection, recognition, and tracking

Korshunov

Ooi

2011

ACM Trans. Multimedia Comput. Commun. Appl.

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Many distributed multimedia applications rely on video analysis algorithms for automated video and image processing. Little is known, however, about the minimum video quality required to ensure an accurate performance of these algorithms. In an attempt to understand these requirements, we focus on a set of commonly used face analysis algorithms. Using standard datasets and live videos, we conducted experiments demonstrating that the algorithms show almost no decrease in accuracy until the input video is reduced to a certain critical quality, which amounts to significantly lower bitrate compared to the quality commonly acceptable for human vision. Since computer vision percepts video differently than human vision, existing video quality metrics, designed for human perception, cannot be used to reason about the effects of video quality reduction on accuracy of video analysis algorithms. We therefore investigate two alternate video quality metrics, blockiness and mutual information, and show how they can be used to estimate the critical video qualities for face analysis algorithms.

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Section: Related Workmentioning

confidence: 99%

Video quality for face detection, recognition, and tracking

Korshunov

Ooi

2011

ACM Trans. Multimedia Comput. Commun. Appl.

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“…Generally, there exist three transcoding architectures for the bit-rate transcoding: open-loop transcoders [23]- [25], cascaded pixel-domain transcoders (CPDTs) [24], [26] and DCT-domain transcoders (DDTs) [27]- [29]. The open-loop architectures include selective transmission [23], [24], where the high-frequency DCT coefficients are discarded, and requantization [24], [25], where the DCT coefficients are requantized.…”

Section: Bit-rate Transcodingmentioning

confidence: 99%

“…The open-loop architectures include selective transmission [23], [24], where the high-frequency DCT coefficients are discarded, and requantization [24], [25], where the DCT coefficients are requantized. Fig.…”

Section: Bit-rate Transcodingmentioning

confidence: 99%

Digital Video Transcoding

2005

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“…It was proposed in [13,14] a DCT decimation scheme that extracts the 4x4 lowfrequency DCT coefficients from the four original blocks b 1 -b 4 , then performs 4x4 IDCT to obtain four 4x4 subblocks, and finally combine the four subblocks into an 8x8 blocks. This approach was shown to achieve significant performance improvement over the filtering schemes [14].…”

Section: B Dct Decimationmentioning

confidence: 99%

“…It is, however, very computationally intensive for real-time applications, even though the motion-vectors and coding-modes of the incoming bit-stream can be reused for fast processing. [2][3][4][5][6][7][8][9][10][11][14][15]. In [10], a simplified pixel-domain transcoder (SPDT) was proposed to reduce the computational complexity of the cascade transcoder by reusing motion vectors and merging the decoding and encoding process and eliminating the IDCT and MC (Motion Compensation) operations.…”

Section: Introductionmentioning

confidence: 99%

A DCT-Domain Video Transcoder for Spatial Resolution Downconversion

Lee

Lin

Kao

2002

Recent Advances in Visual Information Systems

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Abstract. Video transcoding is an efficient way for rate adaptation and format conversion in various networked video applications. Several transcoder architectures have been proposed to achieve fast processing. Recently, thanks to its relatively low complexity, the DCT-domain transcoding schemes have become very attractive. In this paper, we investigate efficient architectures for video downscaling in the DCT domain. We propose an efficient method for composing downscaled motion vectors and determining coding modes. We also present a fast algorithm to extract partial DCT coefficients in the DCT-MC operation and a simplified cascaded DCT-domain video transcoder architecture.

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Constrained and general dynamic rate shaping of compressed digital video

Cited by 78 publications

References 7 publications

Video quality for face detection, recognition, and tracking

Video quality for face detection, recognition, and tracking

Digital Video Transcoding

A DCT-Domain Video Transcoder for Spatial Resolution Downconversion

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