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We present a novel No-Reference (FR) video quality assessment
(VQA) algorithm that operates on the sparse representation
coefficients of local spatio-temporal (video) volumes.
Our work is motivated by the observation that the primary
visual cortex adopts a sparse coding strategy to represent
visual stimulus. We use the popular K-SVD algorithm to construct
spatio-temporal dictionaries to sparsely represent local
spatio-temporal volumes of natural videos. We empirically
demonstrate that the histogram of the sparse representations

In an HTTP streaming framework, continuous time quality evaluation is necessary to monitor the time-varying subjective quality (TVSQ) of the videos resulting from rate adaptation. In this paper, we present a novel learning framework for TVSQ assessment using linear regression under the Reduced-Reference (RR) and the No-Reference (NR) settings. The proposed framework relies on objective short time quality estimates and past TVSQs for predicting the present TVSQ.

Multi-modality based adult video detection is an effective approach of filtering pornography. However, existing methods lack accurate representation methods of multi-modality semantics. Addressing at the issue, we propose a novel method of bimodal codebooks based adult video detection. Firstly, the audio codebook is created by periodicity analysis from the labeled audio segments. Secondly, the visual codebook is generated by detecting regions-of-interest (ROI) on the basis of saliency analysis.

Continuous, real-time bit error ratio (BER) test of modern communication and storage channels is a ubiquitous problem: the noises tend to vary in space and time, and are difficult to fully characterize offline. Traditional method requires time consuming accumulation of samples for which Bayesian method has shown its promise in alleviating by incorporating a priori knowledge. However, the method has so far depended on a simplistic linear search algorithm that suffers from long running time which defeats the purpose of sample reduction.

Artery/vein classification in fundus images is a prerequisite for the assessment of diseases such as diabetes, hypertension or other cardiovascular pathologies. One clinical measure used to assess the severity of cardiovascular risk is the retinal arterio-venous ratio (AVR), which significantly depends on the accuracy of vessel classification into arteries or veins. This paper proposes a novel method for artery/vein classification combining deep learning and graph propagation strategies.

The objective of this paper is to extract robust features for
detecting replay spoof attacks on text-independent speaker
verification systems. In the case of replay attacks, prere-
corded utterance of the target speaker is played to the auto-
matic speaker verification system (ASV)to gain unauthorized
access. In such a scenario, the speech signal carries the char-
acteristics of the intermediate recording device as well. In the
proposed approach, the characteristics of the intermediate de-

There have been several studies, in the recent past, pointing to the
importance of analytic phase of the speech signal in human percep-
tion, especially in noisy conditions. However, phase information is
still not used in state-of-the-art speech recognition systems. In this
paper, we illustrate the importance of analytic phase of the speech
signal for automatic speech recognition. As the computation of ana-
lytic phase suffers from inevitable phase wrapping problem, we ex-
tract features from its time derivative, referred to as instantaneous

Load monitoring techniques determine the appliances that are turned ON within a given period of time in a household or workplace. They can help occupants optimize their
power consumption behavior. Load monitoring is broadly classified as intrusive or nonintrusive. Intrusive load monitoring requires the attachment of individual sensors to each