Random noise is an inherent problem with all recorded images; the result is often called picture grain. Not only does noise get introduced during post-production editing or the final stage of video compression, but it is also present at the source in the form of film grain or imaging-sensor noise. Noise-reduction algorithms can minimize the grain in a picture.

The simplest approach to noise reduction is to use a spatial filter that removes high-frequency data. In this approach, only a single frame is evaluated at any given time, and parts of the image that are one or two pixels in size are nearly eliminated. This does remove the noise, but it also degrades the image quality because there is no way to differentiate between noise and detail. This approach can also cause an artificial appearance in which people look like their skin is made of plastic. This represents the most widely used noise-reduction approach.

A temporal filter takes advantage of the fact that noise is a random element of the image that changes over time. Instead of simply evaluating individual frames, a temporal noise filter evaluates several frames at once. By identifying the differences between two frames and then removing that data from the final image, visible noise can be reduced very effectively. If there are no objects in motion, this is a virtually perfect noise-reduction technique that preserves most of the detail. This approach is used by many high-end competitors.

However, a problem arises if there are moving objects in the image, which also cause differences from one frame to the next; of course, these differences should be retained. If moving objects are not distinguished from noise, a ghosting or smearing effect is seen.

HQV processing uses a per-pixel motion-adaptive and noise-adaptive temporal filter to avoid the artificial appearance and artifacts associated with conventional noise filters. To preserve maximum detail, moving pixels do not undergo unnecessary noise processing. In static areas, the strength of noise reduction is determined on a per-pixel basis, depending on the level of noise in the surrounding pixels as well as in previous frames, allowing the filter to adapt to the amount of noise in the image at any given time. The end result is a natural-looking picture with minimal noise and grain and maximum preservation of fine details.