In theory, DTV’s picture quality is superior to traditional Analog TV: no more “ghosting”, “snow”, “judder”, “Never The Same Color”, etc. Nevertheless, analog signals’ arguably most glaring weakness is its blurriness and lack of fine image details due to shortcomings in high frequency response, or simply put, in bandwidth. The more detailed an image is or the more resolution it has, the more bandwidth it needs.
Long ago, it was agreed upon that 6 megahertz (MHz) of bandwidth from the allowed spectrum would be allocated to each channel for broadcasters in the U.S. to provide these analog TV signals. This limitation in video bandwidth and its corresponding standard (NTSC) in turn dictated the specifications of the traditional TV set, as well as its picture quality for decades.
With the advent of DTV, broadcasters saw a great opportunity to make much better use of their bandwidth. Indeed, from their standpoint, one of the sterling advantages of DTV was that it allowed for multiple channels in the same amount of bandwidth, and would later on allow for High-Definition Programming (HDTV).
Too many bits
HDTV means a surge in technical requirements. A conventional NTSC signal has 525 lines scanned at 29.97Hz for a 4.2MHz minimum bandwidth to carry the analog video off of a 6MHz channel. When digitized and compressed, this signal can be recorded on a DVD and its bit rate varies from 2 to 10 Mbits/s (adaptive) with an average of 4 Mbits/s. For comparison, a typical HDTV feed has roughly 5 times the resolution. All things being equal, the transmitted bit rate should be around 5 times more important to deliver similar performance.
Whether it’s the traditional over-the-air (OTA) broadcast, the cable company’s set-top box or the satellite-TV provider, they all have a limited amount of bandwidth to send all these feeds, to which, they add other bandwidthintensive services such as interactive broadcasts, subscription channels, TV schedules, etc.
So what is the solution? Compression.
Digital Video Compression Artifacts
The most commonly used method today to compress digital video data is MPEG-2. From current satellite streams and digital cable feeds to off-the-air digital broadcasts, MPEG-2 has now been internationally adopted for a variety of applications.
MPEG-2 first exploits temporal redundancy through motion estimation and then proceeds to spatially subdivide the image in 8x8 blocks upon which the DCT (Discrete Cosine Transform) is applied to exploit spatial redundancy. Compression is done by quantizing resulting DCT coefficients and re-ordering them to maximize the probability of long runs of zeroes, and then run-length coded. Finally a Huffman encoding scheme is used. The whole process allows for great savings in terms of bit-rate ratio (>10:1).
However, these savings don’t come free, and because the codec discards some of the original video information, there can be serious side-effects; MPEG-2 is what we call a lossy codec. It discards image information believed to be of lesser visual importance. The more you want to compress, the further away you get from the look of the original image. Image quality and fidelity now depends on the chosen (or often imposed) level of compression. And since that is directly tied to the available bandwidth, we must ask ourselves when is the video simply too compressed?
Visible Artifacts
Bandwidth restrictions in the digital domain, combined with an aggressive image compression scheme, will manifest themselves differently than in the analog world.
Usually, the analog degradation (or noise) will more often than not follow a Gaussian distribution. This distribution’s advantage is that it will preserve essential content and mimic our eyes’ drop-off. We usually find a constrained analog image a bit fuzzy but nothing clearly objectionable.
Digital noise follows a different distribution pattern and, more importantly, has a particular shape that the human perception finds unnatural. There are mainly two artifacts who present the latter characteristic when pushing the limits of MPEG-2 (or any DCT block-based codec): Mosquito noise and Blocking artifacts.
Mosquito noise, a.k.a. Gibbs effect
Mosquito noise is most apparent around artificial or CG (Computer Generated) objects or scrolling credits (lettering) on a plain coloured background. It appears as some haziness and/or shimmering around high-frequency content (sharp transitions between foreground entities and the background or hard edges) and can sometimes be mistaken for ringing Unfortunately, this peppered effect is also visible around more natural shapes like a human body. The VIRIS project (a Video Reference Impairment System) defines mosquito noise as follows: "Form of edge busyness distortion sometimes associated with movement, characterized by moving artifacts and/or blotchy noise patterns superimposed over the objects (resembling mosquito flying around a person's head and shoulders)."
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Mosquito noise
It occurs when reconstructing the image and approximating discarded data by inversing the transform model (iDCT).
"Mosquitoes" can also be found in other areas of an image. For instance, the presence of a very distinct texture or film grain at compression will also introduce mosquito noise. The result will be somewhat similar to random noise; the mosquitoes will seem to blend with the texture or the film grain and can look like original features of the picture.
Next: Blocking Artifacts
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