Abstract: A method and system for automated video quality assessment which reduces the adverse effects of sub-field/frame misalignments between the reference and test sequences. More particularly, the invention provides for misalignments down to a sub-field/frame level to be handled by individually matching sub-field/frame elements of a test video field/frame with sub-field/frame elements from a reference video field/frame. The use of a matching element size that is significantly smaller than the video field/frame size enables transient sub-field/frame misalignments to be effectively tracked.

Abstract: A system and method for evaluating a video signal comprising a plurality of video frames. In the plurality video frames, the number of pixels changed from one frame to the next are analysed and each frame is identified as being a repeat if a set proportion of the pixels present in the current frame match those present in the previous frame. The resultant pattern of repeat frames in the video signal is recorded. The resultant pattern is then scanned to determine whether or not a given repeat frame is part of a temporally repetitive pattern and those repeat frames not determined to be part of the temporally repetitive pattern are identified. As result of the above, video quality assessment is able to treat these two types of temporal artefact separately so as to distinguish between the almost invisible regular frame loss and the visually disturbing irregular frame loss.

Abstract: A system and method for evaluating a video signal comprising a plurality of video frames. In the plurality video frames, the number of pixels changed from one frame to the next are analysed and each frame is identified as being a repeat if a set proportion of the pixels present in the current frame match those present in the previous frame. The resultant pattern of repeat frames in the video signal is recorded. The resultant pattern is then scanned to determine whether or not a given repeat frame is part of a temporally repetitive pattern and those repeat frames not determined to be part of the temporally repetitive pattern are identified. As result of the above, video quality assessment is able to treat these two types of temporal artefact separately so as to distinguish between the almost invisible regular frame loss and the visually disturbing irregular frame loss.

Abstract: A non-intrusive test method for video sequences correlates a number of quality indicators derived from a sequence with a matrix of constants to predict a quality score for the sequence.

Abstract: A method of aligning a first image with a second image, the method includes approximately locating the second image; scaling the first image to the approximate size of the second image; positioning the scaled first image over the approximate location of the second image; generating a measure of fit between the first image and the second image; re-positioning the second image and repeating the above steps to obtain an increased measure of fit value.

Abstract: A non-intrusive test method for video sequences is described that correlates a number of quality indicators derived from a sequence with a matrix of constants to predict a quality score for the sequence

Abstract: A method of aligning a first image with a second image, the method comprising the steps of; approximately locating the second image; scaling the first image to the approximate size of the second image; positioning the scaled first image over the approximate location of the second image; generating a measure of fit between the first image and the second image; re-positioning the second image and repeating the above steps to obtain an increased measure of fit value.