In realtime communications, bandwidth and computation complexity are two deciding factors for encoding algorithms, and eventually, encoding quality. Cameras with a variety of quality levels produce different levels of noise in the capturing process, which often increases bit-rate when encoding. Many temporal algorithms are proposed to try to reduce noise in the different levels. Some create ghost effects regardless of the motion. Other algorithms do a good job but at the expense of high computational complexity for estimating motion.
Thus, better video codec efficiency is desired that provides the same perceived video quality at the least possible bandwidth, and improves video quality by reducing noise, producing sharper edges, more vivid colors, and so on. Compression also needs to be more efficient at least with respect to reducing the bits per second to represent the same video data. Conventional techniques also introduce temporal noise across images thereby confusing the motion estimation. The codecs can then misunderstand the noise, and thus, consider the noise important motion vectors. Moreover, if the noise is not filtered out, bandwidth is expended on representing noise rather than real information.
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