Image-Dependent Spatial Shape-Error Concealment

Existing spatial shape-error concealment techniques are broadly based upon either parametric curves that exploit geometric information concerning a shape's contour or object shape statistics using a combination of Markov random fields and maximum a posteriori estimation. Both categories are to some extent, able to mask errors caused by information loss, provided the shape is considered independently of the image/video. They palpably however, do not afford the best solution in applications where shape is used as metadata to describe image and video content. This paper presents a novel image-dependent spatial shape-error concealment (ISEC) algorithm that uses both image and shape information by employing the established rubber-band contour detecting function, with the novel enhancement of automatically determining the optimal width of the band to achieve superior error concealment. Experimental results corroborate both qualitatively and numerically, the enhanced performance of the new ISEC strategy compared with established techniques

Survey of Error Concealment techniques: Research directions and open issues

© 2015 IEEE. Error Concealment (EC) techniques use either spatial, temporal or a combination of both types of information to recover the data lost in transmitted video. In this paper, existing EC techniques are reviewed, which are divided into three categories, namely Intra-frame EC, Inter-frame EC, and Hybrid EC techniques. We first focus on the EC techniques developed for the H.264/AVC standard. The advantages and disadvantages of these EC techniques are summarized with respect to the features in H.264. Then, the EC algorithms are also analyzed. These EC algorithms have been recently adopted in the newly introduced H.265/HEVC standard. A performance comparison between the classic EC techniques developed for H.264 and H.265 is performed in terms of the average PSNR. Lastly, open issues in the EC domain are addressed for future research consideration

Resilient transmission of H.264/AVC video sequences using probabilistic neural networks

H.264/AVC is expected to become an essential component in the delivery of wireless multimedia content. While achieving high compression ratios, this codec is extremely vulnerable to transmission errors. These errors generally result in spatio-temporal propagation of distorted macroblocks (MBs) which significantly degrade the perceptual quality of the reconstructed video sequences. This paper presents a scheme for resilient transmission of H.264/AVC streams in noisy environments. The proposed algorithm exploits the redundant information which is inherent in the neighboring MBs and applies a Probabilistic Neural Network (PNN) classifier to detect visually impaired MBs. This algorithm achieves Peak Signal-to-Noise Ratio (PSNR) gains of up to 14.29 dB when compared to the standard decoder. Moreover, this significant gain in quality is achieved with minimal overheads and no additional bandwidth requirement, thus making it suitable for conversational and multicast/ broadcast services where feedback-based transport protocols cannot be applied.peer-reviewe

Enhancing the error detection capabilities of DCT based codecs using compressed domain dissimilarity metrics

Video compression standards are implemented in wireless data transmission technologies to provide multimedia services efficiently. These compression standards generally utilize the Discrete Cosine Transform (DCT) in conjunction with variable length codes (VLC) in order to achieve the required high compression ratios. While providing the necessary high data rates, this technique has the disadvantage of making the system more susceptible to transmission errors. The standard decoders do not manage to detect a large number of corrupted macroblocks, 40.54% not detected for H.263+, contributing to a significant reduction in the end-to-end video quality as perceived by the end-user. This paper presents three dissimilarity metrics which contain both color and texture information and that can be extracted directly from the compressed DCT coefficients. These metrics can be used to enhance the error-detection capabilities of standard DCT based codecs. Simulation results show that the proposed algorithm increases the error detection rate by 54.06% with a gain in peak signal-to-noise ratio (PSNR) of 3.21 dB. This improvement in performance is superior to other solutions found in literature.peer-reviewe

Improved quality of experience of reconstructed H.264/AVC encoded video sequences through robust pixel domain error detection

The transmission of H.264/AVC encoded sequences over noisy wireless channels generally adopt the error detection capabilities of the transport protocol to identify and discard corrupted slices. All the macroblocks (MBs) within each corrupted slice are then concealed. This paper presents an algorithm that does not discard the corrupted slices but tries to detect those MBs which provide major visual artefacts and then conceal only these MBs. Results show that the proposed solution, based on a set of image-level features and two Support Vector Machines (SVMs), manages to detect 94.6% of those artefacts. Gains in Peak Signal-to-Noise Ratios (PSNR) of up to 5.74 dB have been obtained when compared to the standard H.264/AVC decoder.peer-reviewe

Enhancing the error detection capabilities of the standard video decoder using pixel domain dissimilarity metrics

The video compression standards commonly adopted in wireless multimedia services utilize variable length codes (VLC) in order to attain high compression ratios. While providing the high data rates required, this technique makes the system more susceptible to transmission errors. Thus the end-to-end quality of the video stream transmitted over an error-prone channel depends on the detection, and concealment of the corrupted macroblocks. The error detection capability of standard decoders is quite limited, for example, in the case of the H.263+ codec around 40.54% of the corrupted macroblocks are undetected, placing a bound on the perceived quality of the reconstructed video sequence. This paper presents a novel solution using eight pixel domain dissimilarity metrics computed in the CIE LUV color space which can be used at decode time to improve the error detection rate of the standard decoder. The spatial dissimilarity metric has been found to perform the best with an average increase in error detection rate of 60.38% when compared to the standard decoder (about 20% more than other published results) with 0% of false detection and a gain in peak signal-to-noise ratio (PSNR) of 3.94 dB.peer-reviewe

Global Motion Estimation and Its Applications

In this chapter, global motion estimation and its applications are given. Firstly we give the definitions of global motion and global motion estimation. Secondly, the parametric representations of global motion models are provided. Thirdly, global estimation approaches including pixel domain based global motion estimation, hierarchical globa