Quality assessment methods are classified into three types depending on the availability of the reference image or video:
full-reference (FR), reduced-reference (RR), or no-reference (NR). This paper proposes efficient RR visual quality
metrics, called motion vector histogram based quality metrics (MVHQMs). In assessing the visual quality of a video, the
overall impression of a video tends to be regarded as the visual quality of the video. To compare two motion vectors
(MVs) extracted from reference and distorted videos, we define the one-dimensional (horizontal and vertical) MV
histograms as features, which are computed by counting the number of occurrences of MVs over all frames of a video.
For testing the similarity between MV histograms, two different MVHQMs using the histogram intersection and
histogram difference are proposed. We evaluate the effectiveness of the two proposed MVHQMs by comparing their
results with differential mean opinion score (DMOS) data for 46 video clips of common intermediate format
(CIF)/quarter CIF (QCIF) that are coded under varying bit rates/frame rates with H.263. We compare the performance of
the proposed metrics and conventional quality measures. Experimental results with various test video sequences show
that the proposed MVHQMs give better performance than the conventional methods in various aspects such as the
performance, stability, and data size.