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The topics of visual and audio quality assessment (QA) have been widely researched for decades, yet nearly all of this prior work has focused only on single-mode visual or audio signals. However, visual signals rarely are presented without accompanying audio, including heavy-bandwidth video streaming applications. Moreover, the distortions that may separately (or conjointly) afflict the visual and audio signals collectively shape user-perceived quality of experience (QoE). This motivated us to conduct a subjective study of audio and video (A/V) quality, which we then used to compare and develop A/V quality measurement models and algorithms. The new LIVE-SJTU Audio and Video Quality Assessment (A/V-QA) Database includes 336 A/V sequences that were generated from 14 original source contents by applying 24 different A/V distortion combinations on them. We then conducted a subjective A/V quality perception study on the database towards attaining a better understanding of how humans perceive the overall combined quality of A/V signals. We also designed four different families of objective A/V quality prediction models, using a multimodal fusion strategy. The different types of A/V quality models differ in both the unimodal audio and video quality prediction models comprising the direct signal measurements and in the way that the two perceptual signal modes are combined. The objective models are built using both existing state-of-the-art audio and video quality prediction models and some new prediction models, as well as quality-predictive features delivered by a deep neural network. The methods of fusing audio and video quality predictions that are considered include simple product combinations as well as learned mappings. Using the new subjective A/V database as a tool, we validated and tested all of the objective A/V quality prediction models. We will make the database publicly available to facilitate further research.Existing two-dimensional (2-D) autofocus algorithms, exploiting the known structure of 2-D phase error, lack the error estimation accuracy in the case of a low signal-tonoise ratio (SNR) due to the non-exhaustive use of the echo signal. In this paper, we propose a novel 2-D autofocus algorithm, thoroughly utilizing all the available data and therefore achieving superior estimation performance. Via analytical study, we show that the partial derivative of the 2-D error with respect to the azimuth frequency is approximable as a function of single argument, after appropriate change of variable. NSC16168 solubility dmso The established property enables a scheme for the azimuth phase error (APE) measurement, where the polar formatted data are fragmented along the range frequency and then aligned along the azimuth frequency, in order to equalize phase gradients in different fragments. On the one hand, such a scheme avoids the necessity to divide the full-aperture signal into subapertures, while on the other hand, it involves the whole signal support. Improved accuracy of the resulting estimate is achieved through the joint inter-fragment estimation of the APE gradient. The proposed algorithm, based on the mentioned scheme, was validated via computer simulations. The conducted experiments confirmed its preference against the existing techniques. The preference is particularly distinct for low SNR imagery.This paper presents a novel optimized quantization constraint set, acting as an add-on to existing DCT-based image restoration algorithms. The constraint set is created based on generalized Gaussian distribution which is more accurate than the commonly used uniform, Gaussian or Laplacian distributions when modeling DCT coefficients. More importantly, the proposed constraint set is optimized for individual input images and thus it is able to enhance image quality significantly in terms of signal-to-noise ratio. Experimental results indicate that the signal-to-noise ratio is improved by at least 6.78% on top of the existing state-of-the-art methods, with a corresponding expense of only 0.38% in processing time. The proposed algorithm has also been implemented in GPU, and the processing speed increases further by 20 times over that of CPU implementation. This makes the algorithm well suited for fast image retrieval in security and quality monitoring system.A key for person re-identification is achieving consistent local details for discriminative representation across variable environments. Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment. This paper proposes a receptive multi-granularity learning approach to facilitate stripe-based feature learning. This approach performs local partition on the intermediate representations to operate receptive region ranges, rather than current approaches on input images or output features, thus can enhance the representation of locality while remaining proper local association. Toward this end, the local partitions are adaptively pooled by using significance-balanced activations for uniform stripes. Random shifting augmentation is further introduced for a higher variance of person appearing regions within bounding boxes to ease misalignment. By twobranch network architecture, different scales of discriminative identity representation can be learned. In this way, our model can provide a more comprehensive and efficient feature representation without larger model storage costs. Extensive experiments on intra-dataset and cross-dataset evaluations demonstrate the effectiveness of the proposed approach. Especially, our approach achieves a state-of-the-art accuracy of 96.2%@Rank-1 or 90.0%@mAP on the challenging Market-1501 benchmark.We demonstrate time and frequency transfer using a White Rabbit time transfer system over millimeter-wave (mmwave) 71-76 GHz carriers. To validate the performance of our system, we present overlapping Allan deviation, time deviation, and phase statistics. Over mm-wave carriers, we report an ADEV of 71 × 10-12 at 1 second and a TDEV of less then 10 picoseconds at 10 000 seconds. Our results show that after 4 seconds of averaging we have sufficient precision to transfer a cesium atomic frequency standard. We analyze the link budget and architecture of our mm-wave link and discuss possible sources of phase error and their potential impact on the White Rabbit frequency transfer. Our data shows that White Rabbit can synchronize new network architectures, such as physically separated fiber-optic networks and support new applications such as the synchronization of intermittently connected platforms. We conclude with recommendations for future investigation including cascaded hybrid wireline and wireless architectures.
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