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Wyner-Ziv side information generation using a higher order piecewise trajectory temporal interpolation algorithm
Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding, to one where the computational cost is largely incurred by the decoder. This enables low-cost, resource-poor sensors to be used at the transmitter in various applications including multi-sensor surveillance. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have generally been based on linear temporal interpolation, though these do not always produce satisfactory SI quality especially in sequences exhibiting asymmetric (non-linear) motion. This paper presents a higher-order piecewise trajectory temporal interpolation (HOPTTI) algorithm for SI generation that quantitatively and perceptually affords better SI quality in comparison to existing temporal interpolation-based approaches
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
Statistical framework for video decoding complexity modeling and prediction
Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, SlepianâWolf and WynerâZiv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
Adaptive temporal decimation algorithm with dynamic time window
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Multiple description video coding for stereoscopic 3D
In this paper, we propose an MDC schemes for stereoscopic 3D video. In the literature, MDC has previously been applied in 2D video but not so much in 3D video. The proposed algorithm enhances the error resilience of the 3D video using the combination of even and odd frame based MDC while retaining good temporal prediction efficiency for video over error-prone networks. Improvements are made to the original even and odd frame MDC scheme by adding a controllable amount of side information to improve frame interpolation at the decoder. The side information is also sent according to the video sequence motion for further improvement. The performance of the proposed algorithms is evaluated in error free and error prone environments especially for wireless channels. Simulation results show improved performance using the proposed MDC at high error rates compared to the single description coding (SDC) and the original even and odd frame MDC
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