Evaluation of temporal variation of video quality in packet loss networks

a b s t r a c t We examine the effect that variations in the temporal quality of videos have on global video quality. We also propose a general framework for constructing temporal video quality assessment (QA) algorithms that seek to assess transient temporal errors, such as packet losses. The proposed framework modifies simple frame-based quality assessment algorithms by incorporating a temporal quality variance factor. We use packet loss from channel errors as a specific study of practical significance. Using the PSNR and the SSIM index as exemplars, we are able to show that the new video QA algorithms are highly responsive to packet loss errors

Reducing False Positives of a Bloom Filter using Cross-Checking Bloom Filters

A Bloom filter is a compact data structure that supports membership queries on a set, allowing false positives. The simplicity and the excellent performance of a Bloom filter make it a standard data structure of great use in many network applications. In reducing the false positive rate of a Bloom filter, it is well known that the size of a Bloom filter and accordingly the number of hash indices should be increased. In this paper, we propose a new architecture reducing the false positive rate of a Bloom filter more efficiently. The proposed architecture uses cross-checking Bloom filters that are queried in case of positives of a main Bloom filter to cross-check the results. If every cross-checking Bloom filters produce negatives, the positive of the main Bloom filter can be determined as a false positive. The main Bloom filter is not necessarily large to reduce the false positive rate, since more numbers of the false positives of the main Bloom filter are identified by cross-checking Bloom filters. Simulation results show that the false positive of the proposed scheme converges to zero faster, while requiring the total memory size for Bloom filters smaller, than that of a single Bloom filter architecture

Priority Area-based Quad-Tree Packet Classification Algorithm and Its Mathematical Framework

Packet classification is an essential function for next-generation Internet routers to provide high quality of service. Packet classification using multiple header fields is a challenging problem that should be performed at wire speed for all incoming packets. This paper proposes a novel mathematical framework for packet classification problem. Then the priority area-based quad-tree (PAQT) packet classification algorithm combining priority search and recursive space decomposition is formally described using the framework. The validity of the PAQT algorithm is mathematically proved for theoretical justification. The proposed mathematical framework can be applied to other packet classification algorithms for formal description and theoretical justification. Extensive simulation results demonstrate that the PAQT algorithm has very good performance compared to other packet classification algorithms in terms of search speed, memory size, and scalability

Reducing False Positives of a Bloom Filter using

Abstract: A Bloom filter is a compact data structure that supports membership queries on a set, allowing false positives. The simplicity and the excellent performance of a Bloom filter make it a standard data structure of great use in many network applications. In reducing the false positive rate of a Bloom filter, it is well known that the size of a Bloom filter and accordingly the number of hash indices should be increased. In this paper, we propose a new architecture reducing the false positive rate of a Bloom filter more efficiently. The proposed architecture uses cross-checking Bloom filters that are queried in case of positives of a main Bloom filter to cross-check the results. If every cross-checking Bloom filters produce negatives, the positive of the main Bloom filter can be determined as a false positive. The main Bloom filter is not necessarily large to reduce the false positive rate, since more numbers of the false positives of the main Bloom filter are identified by cross-checking Bloom filters. Simulation results show that the false positive of the proposed scheme converges to zero faster, while requiring the total memory size for Bloom filters smaller, than that of a single Bloom filter architecture