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A redundancy gain occurs when perceptually identical stimuli are presented together, resulting in quicker categorization of these paired stimuli than lone stimuli. Similar effects have been reported for paired stimuli within the same conceptual category, particularly if the category is self-related. We recruited 528 individuals across three related studies to investigate whether, during perceptual and conceptual redundancy, such self-bias effects on foreground stimuli are modulated by natural versus urban backgrounds. Here, we highlight our observations pertaining to perceptual and conceptual redundancy effects of the foreground stimuli. In our first experiment, response options were randomised per trial. Results showed reaction time gains for perceptually identical stimuli, but this advantage was not modulated by self/other categorization. However, slower reaction times were observed for conceptually-related stimulus pairs and were influenced by self/other categorization. The second experiment replicated the methods of earlier studies of redundancy and observed comparable results to Experiment 1: a perceptual redundancy gain unmodulated by self/other categorization, yet for conceptual redundancy, no gain/cost but effects of self/other categorization. In the third experiment, self/other categories were substituted with arbitrary A/B categories: Once more, there was a perceptual redundancy gain and no conceptual redundancy gain. Notably, A/B categorisation produced effects equivalent to self/other categorisation. Overall, these findings challenge previous research on the facilitated early processing of conceptually-related stimuli and suggest that self-relatedness may not exert a unique effect on stimulus processing beyond attentional and response preferences during categorization. Our study motivates further research to understand conceptual categorization and redundancy gain effects. [ABSTRACT FROM AUTHOR]

Video compression has become increasingly crucial as video resolution and bitrate have surged in recent years. However, most widely applied video compression methods do not fully exploit the characteristics of the Human Visual System (HVS) to reduce perceptual redundancy in videos. In this paper, we propose a novel video compression method that integrates visual saliency information with foveation to reduce perceptual redundancy. We present a new approach to subsample and restore the input image using saliency data, which allocates more space for salient regions and less for non-salient ones. We analyze the information entropy in video frames before and after applying our algorithm and demonstrate that the proposed method reduces redundancy. Through subjective and objective evaluations, we show that our method produces videos with superior perceptual visual quality. Moreover, our approach can be added to most existing video compression standards without altering their bitstream format.

The just noticeable difference (JND) model reflects the visibility limitations of the human visual system (HVS), which plays an important role in perceptual image/video processing and is commonly applied to perceptual redundancy removal. However, existing JND models are usually constructed by treating the color components of three channels equally, and their estimation of the masking effect is inadequate. In this paper, we introduce visual saliency and color sensitivity modulation to improve the JND model. Firstly, we comprehensively combined contrast masking, pattern masking, and edge protection to estimate the masking effect. Then, the visual saliency of HVS was taken into account to adaptively modulate the masking effect. Finally, we built color sensitivity modulation according to the perceptual sensitivities of HVS, to adjust the sub-JND thresholds of Y, Cb, and Cr components. Thus, the color-sensitivity-based JND model (CSJND) was constructed. Extensive experiments and subjective tests were conducted to verify the effectiveness of the CSJND model. We found that consistency between the CSJND model and HVS was better than existing state-of-the-art JND models. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) has been widely used to collect data from the wireless sensor node in fields. Some problems can be solved in this case, such as no network infrastructure in farmland, fast power consumption of multi-hop data forwarding, premature death of nodes near the gateway, and shortened network life cycle. However, the multiple nodes overlapping can often occur during UAVs collection at the same time, due to the possible redundancy of adjacent sensor data. In this study, a UAV data collection method was proposed to plan the node selection, hovering position, and collecting order using improved deep reinforcement learning. The UAV data collection from the sensor nodes was then divided into two scenarios: data collection from the partial nodes under perceptual redundancy coverage, and data collection from all nodes. The optimization was made to save the UAV energy consumption in less mission completion time. The data collection of partial nodes under perceived redundancy coverage was suitable for the relatively high proportion of redundant coverage area among nodes. The UAV energy also failed to complete the data collection tasks of all nodes, indicating the low requirements of data integrity. By contrast, the all-node data collection fully met the high requirement of data integrity. In the scenario of partial node data collection with perceived redundant coverage, the Dueling Double Deep Q Network (DDDQN) was used to select the collection nodes and then plan the collecting order, indicating the high energy efficiency of the UAV with the less redundant data. Simulation results show that the DDDQN presented greater data coverage and lower effective coverage average energy consumption than the Deep Q Network (DQN) under the same configuration. The training process of DDDQN was more stable than that of DQN, particularly for the higher returns at the end of learning. More importantly, the flight distance and energy consumption of the DDDQN were reduced by 1.21 km, and 27.9%, respectively, compared with the DQN. In the scenario of all-node data collection, a Double Deep Reinforcement Learning (DDRL) was proposed to optimize the hovering position and UAV collection sequence, in order to minimize the total energy consumption of the UAV during data collection. A comparison was made on the DDRL with the classical PSO-TSP and MEFC. A systematic evaluation was made to clarify the impact of the UAV flight speed on the total energy consumption and total working time, the impact of different node data loads on the UAV energy consumption, the impact of different flight speeds on the UAV hover collection time, and the impact of the number of sensor nodes on the total energy consumption. The simulation results show that the total energy consumption of the improved model was at least 6.3% less than that of the classical PSO-based Travel Salesman Problem (PSO-TSP), and the Minimized Energy Flight Control (MEFC) under different node numbers and UAV flight speeds, especially at the data load of a single node less than 160 kB. Finally, the flight and hover powers of the quadrotor UAV were tested to determine the packet loss rate and received signal strength of the UAV in the field experiments. The actual field flight experiments were carried out on the DDRL and the data collection of the classical PSO-TSP. Field experiment results show that the DDRL-based data collection was reduced by 11.5% for the total energy consumption of UAV, compared with the PSO-TSP. The DDDQN and DDRL approaches can be expected to provide the optimal energy consumption for the UAVs' data collection of wireless sensor nodes in the field. [ABSTRACT FROM AUTHOR]

The just noticeable difference (JND) model reflects the visibility limitations of the human visual system (HVS), which plays an important role in perceptual image/video processing and is commonly applied to perceptual redundancy removal. However, existing JND models are usually constructed by treating the color components of three channels equally, and their estimation of the masking effect is inadequate. In this paper, we introduce visual saliency and color sensitivity modulation to improve the JND model. Firstly, we comprehensively combined contrast masking, pattern masking, and edge protection to estimate the masking effect. Then, the visual saliency of HVS was taken into account to adaptively modulate the masking effect. Finally, we built color sensitivity modulation according to the perceptual sensitivities of HVS, to adjust the sub-JND thresholds of Y, Cb, and Cr components. Thus, the color-sensitivity-based JND model (CSJND) was constructed. Extensive experiments and subjective tests were conducted to verify the effectiveness of the CSJND model. We found that consistency between the CSJND model and HVS was better than existing state-of-the-art JND models.

In this paper, we propose perceptual stereoscopic video coding using a disparity just-noticeable-distortion (JND) model. We obtain the disparity JND model in stereo videos by disparity masking effects of the human visual system (HVS). The disparity JND model represents the maximum distortion of stereo perception that HVS cannot perceive. Based on the disparity JND model, we adjust prediction residuals to remove the perceptual redundancy of stereo videos. Thus, we achieve significant bit-rate saving while maintaining visual quality. Experimental results demonstrate that the proposed method significantly improves coding efficiency without loss of stereoscopic perceptual quality . [ABSTRACT FROM AUTHOR]

Transparency and robustness are two conflicting requirements demanded by digital image watermarking for copyright protection and many other purposes. A feasible way to simultaneously satisfy the two conflicting requirements is to embed high-strength watermark signals in the host signals that can accommodate the distortion due to watermark insertion as part of perceptual redundancy. The search of distortion-tolerable host signals for watermark insertion and the determination of watermark strength are, hence, crucial to the realization of a transparent yet robust watermark. This paper presents a color image watermarking scheme that hides watermark signals in most distortion-tolerable signals within three color channels of the host image without resulting in perceivable distortion. The distortion-tolerable host signals or the signals that possess high perceptual redundancy are sought in the wavelet domain for watermark insertion. A visual model based upon the CIEDE2000 color difference equation is used to measure the perceptual redundancy inherent in each wavelet coefficient of the host image. By means of quantization index modulation, binary watermark signals are embedded in qualified wavelet coefficients. To reinforce the robustness, the watermark signals are repeated and permuted before embedding, and restored by the majority-vote decision making process in watermark extraction. Original images are not required in watermark extraction. Only a small amount of information including locations of qualified coefficients and the data associated with coefficient quantization is needed for watermark extraction. Experimental results show that the embedded watermark is transparent and quite robust in face of various attacks such as cropping, low-pass filtering, scaling, media filtering, white-noise addition as well as the JPEG and JPEG2000 coding at high compression ratios. [ABSTRACT FROM PUBLISHER]

The features of just noticeable difference (JND) in human spatial perception are often used to remove the perceptual redundancy of spatial parameters in 3D multichannel audio. However in previous spatial parameters quantisation schemes, JND data are not effectively utilised resulting in either perceptual distortions of spatial images, or wastes of coding bitrates. It is proposed to effectively utilise azimuthal JND to design the quantisation codebook of spatial parameters azimuths, which divides the full circle of 360° to adjacent quantisation intervals, and set the endpoints of quantisation intervals according to JND of quantisation value. With presented scheme, quantisation codebook size has decreased 13 and 57%, respectively, compared with MPEG Surround and SLQP, as well as ignorable perceptual distortion induced. [ABSTRACT FROM AUTHOR]