Intra Coding Strategy for Video Error Resiliency: Behavioral Analysis

One challenge in video transmission is to deal with packet loss. Since the compressed video streams are sensitive to data loss, the error resiliency of the encoded video becomes important. When video data is lost and retransmission is not possible, the missed data should be concealed. But loss concealment causes distortion in the lossy frame which also propagates into the next frames even if their data are received correctly. One promising solution to mitigate this error propagation is intra coding. There are three approaches for intra coding: intra coding of a number of blocks selected randomly or regularly, intra coding of some specific blocks selected by an appropriate cost function, or intra coding of a whole frame. But Intra coding reduces the compression ratio; therefore, there exists a trade-off between bitrate and error resiliency achieved by intra coding. In this paper, we study and show the best strategy for getting the best rate-distortion performance. Considering the error propagation, an objective function is formulated, and with some approximations, this objective function is simplified and solved. The solution demonstrates that periodical I-frame coding is preferred over coding only a number of blocks as intra mode in P-frames. Through examination of various test sequences, it is shown that the best intra frame period depends on the coding bitrate as well as the packet loss rate. We then propose a scheme to estimate this period from curve fitting of the experimental results, and show that our proposed scheme outperforms other methods of intra coding especially for higher loss rates and coding bitrates

Erratum to:cloud-based SVM for food categorization

[No abstract available

Segmental Spectral Decomposition as a Time Persistent Method of BioImpedance Spectroscopy Feature Extraction

BioImpedance Spectroscopy (BIS) have been investigated in many research areas as a method to detect changes in living tissues. However, BIS measurements are known to be hardly reproducible in clinical applications. This article proposes segmental spectral decomposition as a method of extracting reproducible parameters from raw BIS. The efficiency of this method is then compared to conventional Cole-Cole parameter extraction in a classification task

Applied Artificial Intelligence and Machine Learning for Video Coding and Streaming : Editorial

The papers in this special section focus on applied artificial inteligence and machine learning for video coding and media streaming.publishedVersionNon peer reviewe