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• Compressing videos while maintaining acceptable quality is indispensable. • To this aim, a feasible method is to increase Quantization Parameter (QP) of videos under Just Noticeable Distortion (JND). • We propose a JND-based algorithm for QP optimization, in which a saliency detection is introduced to extract regions of interest, a refinement model to predict QP and an ensemble learning method to improve generalization performance. • Experimental results demonstrate that the proposed algorithm has been achieved significant bitrate reduction without sacrificing perceived quality. Compressing videos while maintaining an acceptable level of Quality of Experience (QoE) is indispensable. To this aim, a feasible method is to further increase the Quantization Parameter (QP) of video stream to eliminate visual redundancy, simultaneously utilizing perceptual characteristics of Human Visual System (HVS) to impose a threshold constraint on the maximum QP. In this paper, we employ Just Noticeable Distortion (JND) to characterize the aforementioned threshold constraint, thereby avoiding perceptual loss during QP refinement process. We propose an effective JND-based algorithm for QP optimization, in which a video saliency detection is introduced to extract regions of interest, a refinement model based on a lightweight network is designed to predict QP value and an ensemble learning method to improve generalization performance. Theoretical analysis and experimental results demonstrate that the proposed algorithm has been successfully applied to Versatile Video Coding (VVC) to achieve significant bitrate reduction without sacrificing perceived quality. [ABSTRACT FROM AUTHOR]

Multiple sound sources localization is a hot topic in audio signal processing and is widely utilized in many application areas. This paper proposed a multiple sound sources localization method based on a statistically dominant source component removal (SDSCR) algorithm by soundfield microphone. The existence of the statistically weak source (SWS) among soundfield microphone signals is validated by statistical analysis. The SDSCR algorithm with joint an intra-frame and inter-frame statistically dominant source (SDS) discriminations is designed to remove the component of SDS while reserve the SWS component. The degradation of localization accuracy caused by the existence of the SWS is resolved using the SDSCR algorithm. The objective evaluation of the proposed method is conducted in simulated and real environments. The results show that the proposed method achieves a better performance compared with the conventional SSZ-based method both in sources localization and counting. [ABSTRACT FROM AUTHOR]

This paper aims to tackle the multiple source localization problem using the signals recorded by a B-format microphone in reverberant environments. The proposed approach analyses the energy and phase information of recorded signals in the time–frequency (T-F) domain to present a two-dimensional detection of low-reverberant-single-source (LRSS) points, where the pressure–velocity energy difference and pressure–velocity phase difference are exploited. The direction of arrival (DOA) estimation of actual sources by utilizing the designed weight kernel density estimation are conducted base on the direction cues derived from the LRSS points. Evaluation on simulated data and data recorded in an actual acoustic chamber demonstrates that a better performance can be obtained through the proposed algorithm in highly reverberant environments. [ABSTRACT FROM AUTHOR]