2.5/3G devices should achieve satisfactory QoS, overcoming mobile standards drawbacks. In-service/blind quality monitoring is essential in order to improve perceptual quality according to Human Visual System. Several techniques have been proposed for image/video quality assessment. A novel no-reference quality index which uses an effective HVS model is proposed. Luminance masking, Contrast Sensitivity Function and temporal masking are taken into account with fast in-service algorithms. The proposed index is able to assess blockiness distortion with a fast image-domain measure. Compression/post-processing blurring effects are measured with a standard approach. Moving artifacts distortion is evaluated taking into account standard deviation with respect to a natural image statistical model. Several distortion effects, in wireless noisy channels with low video-streaming/playback bit rates (e.g. edge busyness and image persistence) are evaluated. A multi-level pooling algorithm (block, temporal-window, frame, and sequence levels) is used. Validation tests have been developed in order to assess index performance and computational complexity. The final measure provides human-like threshold-effect and high correlation with subjective data. Low complexity algorithms can be derived for real-time, HVS-based, QoS management for low-power consumer devices. Different distortion effects (e.g. ringing and jerkiness) can be easily included.
No-reference metrics are very useful for In-Service streaming applications. In this paper a blind measure for video quality assessment is presented. The proposed approach takes into account HVS Luminance Masking, Contrast Sensitivity and Temporal Masking. Video distortion level is then computed evaluating blockiness, blurring and moving artifacts. A global quality index is obtained using a multi-dimensional pooling algorithm (block, temporal window, frame, and sequence levels). Different video standard and several compression ratios have been used. A non-linear regression method has been derived, in order to obtain high linear and rank order correlation factors between human observer ratings and the proposed HVS-based index. Validation tests have been developed to assess index performance and computational complexity. Experimental results show that high correlation factors are obtained using the HVS models.