Model-based distortion estimation for perceptual classification of video packets

Vito, Fabio De; Quaglia, Davide; Martini, Julia

doi:10.1109/mmsp.2004.1436421

Cited by 24 publications

(29 citation statements)

References 6 publications

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“…Regarding the ki SGT , this study assumes that the cumulative error propagation of succeeding video frames of VP ki by a geometric progression with common ratio r=1. From [7] we find that the error propagation effect strongly depends on the frame position in the GOP, while it is almost independent of the sequence. On the other hand, ki SGS calculates the ratio of the number of intra-refreshed MBs in VP ki to the number of total MBs in VP ki .…”

Section: Determining Significance Grade Of Video Packetsmentioning

confidence: 91%

Unequal Priority Arrangement for Delivering Streaming Videos over Differentiated Service Networks

Lee

Chang

Chuang

2006

Advances in Image and Video Technology

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Abstract.Video packets have different significances due to the video coding property. When delivering video data without priority strategy over the Internet, it will seriously degrade the received picture quality. This paper proposes an Unequal Priority Arrangement (UPA) mechanism for video transmission on differentiated service networks. UPA determines the priority of a video packet according to the evaluation from both temporal and spatial domains simultaneously. From the temporal domain, UPA evaluates the packet significance based on the temporal position of the packet and the induced error propagation if the packet is lost. From the spatial domain, UPA evaluates the packet importance based on its content, where the ratio of intra-refreshed MBs is used. Moreover, according to the video complexity, UPA can flexibly adjust the weight based on the evaluation results from temporal and spatial domains. Simulation results show that delivering video data with UPA on differentiated service network outperforms traditional temporal-based and spatial-based priority strategies up to 1.8 dB and 1 dB, respectively.

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Section: Determining Significance Grade Of Video Packetsmentioning

confidence: 91%

Unequal Priority Arrangement for Delivering Streaming Videos over Differentiated Service Networks

Lee

Chang

Chuang

2006

Advances in Image and Video Technology

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“…The co-impact of multiple packet loss is modeled in an additive way as in previously discussed approaches because an exhaustive analysis of all possible loss patterns would be computationally intractable. The authors address the limited applicability caused by the large amount of computation needed with a workaround in [26]. They propose to only use AbS to evaluate the distortion introduced in the current frame, while the distortion in succeeding frames is estimated by means of an error propagation model, similar to Babich et al Although the complexity of the modified approach is heavily reduced, its generality is questionable: the model is based on the statistical analysis of several test sequences, but only one GoP structure is considered, a fixed quantization parameter of 28 is used, and plain frame-copy error concealment is assumed.…”

Section: B Sophisticated Prioritization Schemesmentioning

confidence: 99%

“…To evaluate the performance of AbS, necessary calculations were therefore carried out in advance, and the results were fed into the sender's scheduler from a cache. The priorities were not derived using a model-based approach as proposed, e.g., in [26], but exhaustively simulated by entirely decoding the sequence for each NAL unit. Consequently, these values are, although obtained at an unreasonable computational cost, of higher precision than estimates delivered by model-based techniques.…”

Section: A Reference Approachesmentioning

confidence: 99%

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time

Schier

Welzl

2012

IEEE Trans. Multimedia

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Abstract-This work proposes a quality-oriented, real-time capable prioritization technique for media units of H.264/AVC video streams. The derivation of estimates is based on the analysis of the macroblock partitioning, the spatial extents of temporal dependencies, and the length and strength of prediction chains existing among macroblocks, thus incorporating the expected impact of error propagation. It is demonstrated how the prioritization scheme can be beneficially integrated into live streaming systems which are characterized by tight timing constraints, with the focus on content-aware selective automatic repeat request mechanisms. Additionally, it is shown how potentially limited feedback can be used to adapt the estimation process to leverage prediction preciseness. The approach is compared against existing techniques in terms of practicability and efficiency, and tested under independent and bursty loss conditions in a wired and a wireless test setup. Moreover, the performance is examined when low-latency and constant bitrate video settings are enforced by using x264's novel encoding feature periodic-intra-refresh. Results of both experiments and simulations indicate that the proposed technique outperforms all reference techniques in nearly all test cases, and that the video quality can be further improved by incorporating receiver feedback.Index Terms-End-to-end distortion modeling, error propagation, H.264/AVC, live video streaming, packet loss, selective automatic repeat request (ARQ).

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“…We define v p k as the distortion when the packet p of user k in a GOP is lost. If the distortion by packet p of user k in the current frame is defined as d p k , the distortion v p k can be formulated as the multiplication of LEP and d p k as in [12] v p…”

Section: Rate-distortion Function Including Relative Importancementioning

confidence: 99%

“…However it would be impractical to compute the actual distortion of all packets in real-time video transmission due to computational load. To alleviate the computational complexity, [12] presented the hybrid approach. The error concealment (EC) is evaluated with the AbS method, while error propagation (EP) effect is estimated by a statistical model.…”

Section: Introductionmentioning

confidence: 99%

Cross-layer multiuser resource allocation for video communication over OFDM networks

Yim

Kim

2008

Computer Communications

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Model-based distortion estimation for perceptual classification of video packets

Cited by 24 publications

References 6 publications

Unequal Priority Arrangement for Delivering Streaming Videos over Differentiated Service Networks

Unequal Priority Arrangement for Delivering Streaming Videos over Differentiated Service Networks

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time

Cross-layer multiuser resource allocation for video communication over OFDM networks

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