21,911 research outputs found
Extended analysis of motion-compensated frame difference for block-based motion prediction error
2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Motion-Compensated Coding and Frame-Rate Up-Conversion: Models and Analysis
Block-based motion estimation (ME) and compensation (MC) techniques are
widely used in modern video processing algorithms and compression systems. The
great variety of video applications and devices results in numerous compression
specifications. Specifically, there is a diversity of frame-rates and
bit-rates. In this paper, we study the effect of frame-rate and compression
bit-rate on block-based ME and MC as commonly utilized in inter-frame coding
and frame-rate up conversion (FRUC). This joint examination yields a
comprehensive foundation for comparing MC procedures in coding and FRUC. First,
the video signal is modeled as a noisy translational motion of an image. Then,
we theoretically model the motion-compensated prediction of an available and
absent frames as in coding and FRUC applications, respectively. The theoretic
MC-prediction error is further analyzed and its autocorrelation function is
calculated for coding and FRUC applications. We show a linear relation between
the variance of the MC-prediction error and temporal-distance. While the
affecting distance in MC-coding is between the predicted and reference frames,
MC-FRUC is affected by the distance between the available frames used for the
interpolation. Moreover, the dependency in temporal-distance implies an inverse
effect of the frame-rate. FRUC performance analysis considers the prediction
error variance, since it equals to the mean-squared-error of the interpolation.
However, MC-coding analysis requires the entire autocorrelation function of the
error; hence, analytic simplicity is beneficial. Therefore, we propose two
constructions of a separable autocorrelation function for prediction error in
MC-coding. We conclude by comparing our estimations with experimental results
Prediction error image coding using a modified stochastic vector quantization scheme
The objective of this paper is to provide an efficient and yet simple method to encode the prediction error image of video sequences, based on a stochastic vector quantization (SVQ) approach that has been modified to cope with the intrinsic decorrelated nature of the prediction error image of video signals. In the SVQ scheme, the codewords are generated by stochastic techniques instead of being generated by a training set representative of the expected input image as is normal use in VQ. The performance of the scheme is shown for the particular case of segmentation-based video coding although the technique can be also applied to motion-compensated hybrid coding schemes.Peer ReviewedPostprint (published version
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing
Free-viewpoint video conferencing allows a participant to observe the remote
3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint
image is commonly synthesized using two pairs of transmitted texture and depth
maps from two neighboring captured viewpoints via depth-image-based rendering
(DIBR). To maintain high quality of synthesized images, it is imperative to
contain the adverse effects of network packet losses that may arise during
texture and depth video transmission. Towards this end, we develop an
integrated approach that exploits the representation redundancy inherent in the
multiple streamed videos a voxel in the 3D scene visible to two captured views
is sampled and coded twice in the two views. In particular, at the receiver we
first develop an error concealment strategy that adaptively blends
corresponding pixels in the two captured views during DIBR, so that pixels from
the more reliable transmitted view are weighted more heavily. We then couple it
with a sender-side optimization of reference picture selection (RPS) during
real-time video coding, so that blocks containing samples of voxels that are
visible in both views are more error-resiliently coded in one view only, given
adaptive blending will erase errors in the other view. Further, synthesized
view distortion sensitivities to texture versus depth errors are analyzed, so
that relative importance of texture and depth code blocks can be computed for
system-wide RPS optimization. Experimental results show that the proposed
scheme can outperform the use of a traditional feedback channel by up to 0.82
dB on average at 8% packet loss rate, and by as much as 3 dB for particular
frames
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