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Visual quality consistency is one of the most important issues in video quality assessment. When people view a sequential video, they may have an unpleasant perceptual experience if the video has an inconsistent visual quality even though the average visual quality of the video is not compromised. Thus, consistent visual quality control is mostly expected in general video encoding with limited channel bandwidth and buffer resources. However, there still has not been enough study on such an issue. In this paper, a new objective visual quality metric (VQM) is proposed first, which can easily be incorporated into video coding for guiding video coding. Second, a VQM-based window model is proposed to handle the tradeoff between visual quality consistency and buffer constraint in video coding. Third, a window-level rate control algorithm is developed to accomplish visual quality control based on the above two proposals. Finally, experimental results prove that consistent visual quality, high rate-distortion efficiency, accurate bit control, and compliant buffer constraint can be achieved by the proposed rate control algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a novel reduced-reference (RR) video quality assessment (VQA) is proposed by exploiting the spatial information loss and the temporal statistical characteristics of the interframe histogram. From the spatial perspective, an energy variation descriptor (EVD) is proposed to measure the energy change of each individual encoded frame, which results from the quantization process. Besides depicting the energy change, EVD can further simulate the texture masking property of the human visual system (HVS). From the temporal perspective, the generalized Gaussian density (GGD) function is employed to capture the natural statistics of the interframe histogram distribution. The city-block distance (CBD) is used to calculate the histogram distance between the original video sequence and the encoded one. For simplicity, the difference image between adjacent frames is employed to characterize the temporal interframe relationship. By combining the spatial EVD together with the temporal CBD, an efficient RR VQA is developed. Evaluation on the subjective quality video database demonstrates that the proposed method outperforms the representative RR video quality metric and the full-reference VQAs, such as peak signal-to-noise ratio and structure similarity index in matching subjective ratings. This means that the proposed metric is more consistent with the HVS perception. Furthermore, as only a small number of RR features are extracted for representing the original video sequence (each frame requires only one parameter for describing EVD and three parameters for recording GGD), the RR features can be embedded into the video sequences or transmitted through the ancillary data channel, which can be used in the video quality monitoring system. [ABSTRACT FROM PUBLISHER]

Video quality assessment plays a fundamental role in video processing and communication applications. In this paper, we study the use of motion information and temporal human visual system (HVS) characteristics for objective video quality assessment. In our previous work, two types of spatial distortions, i.e., detail losses and additive impairments, are decoupled and evaluated separately for spatial quality assessment. The detail losses refer to the loss of useful visual information that will affect the content visibility, and the additive impairments represent the redundant visual information in the test image, such as the blocking or ringing artifacts caused by data compression and so on. In this paper, a novel full-reference video quality metric is developed, which conceptually comprises the following processing steps: 1) decoupling detail losses and additive impairments within each frame for spatial distortion measure; 2) analyzing the video motion and using the HVS characteristics to simulate the human perception of the spatial distortions; and 3) taking into account cognitive human behaviors to integrate frame-level quality scores into sequence-level quality score. Distinguished from most studies in the literature, the proposed method comprehensively investigates the use of motion information in the simulation of HVS processing, e.g., to model the eye movement, to predict the spatio-temporal HVS contrast sensitivity, to implement the temporal masking effect, and so on. Furthermore, we also prove the effectiveness of decoupling detail losses and additive impairments for video quality assessment. The proposed method is tested on two subjective quality video databases, LIVE and IVP, and demonstrates the state-of-the-art performance in matching subjective ratings. [ABSTRACT FROM AUTHOR]