Image

Now that we have added beautifully rendered text to our wideband audio, it is time to
add image bandwidth. An A4 image scanned at 300 dpi resolution and 24-bit color,
however, produces a 24-Mbyte file—potentially a memory and delivery bandwidth
embarrassment. As a result, we have a choice of lossless or lossy compression.
In lossless compression, all the data in the original image can be completely constructed
in the receiver. Lossless compression is typically used in medical imaging,
image archiving, or for images where any loss of information compromises application
integrity. The problem with lossless compression is that it is hard to achieve compression
rates of more than 2:1 or 3:1.
An example of a lossless compression technique used for storage system optimization
is a dictionary-based scheme developed by Loughborough University and Actel, a
memory product vendor. This compression technique has a learning capability and
builds up a dictionary of previously sent data, which it shares with the receiver. If an
exact match can be made, only the dictionary reference needs to be sent. If an exact
match is not possible, the information is sent literally—that is, with no compression.
In lossy compression, we take the decision that a certain amount of information can be
thrown away. The impact of discarding the information is either not noticeable or it is
acceptable both to the person or device sending or storing the image or to the person
or device receiving or storing the image. Compression ratios of 40:1 or higher are relatively
easy to achieve with lossy compression. Compression schemes tend to be optimized
either to improve storage bandwidth efficiency or delivery bandwidth
efficiency, but not necessarily both.
Image compression standards are codified by the Joint Picture Experts Group, or
JPEG. The Joint Bi-level Image experts Group (JBIG) looks after document compression, document scanning, and optical character recognition (OCR). Bi-level means black and
white, but the group also addresses grayscale compression. JPEG 2000 is the unified standard
covering lossy and lossless compression and introduces the concept of Q factor.
AJPEG image is built up of a number of 8 x 8 pixel blocks that are transformed (like
our audio codec) from the time to the frequency domain. The frequency content of the
image is described by a string of digital coefficients. If one pixel block exactly matches
the next, effectively, a “same again” message is sent. For example, endless blue sky
would produce a whole series of identical pixel blocks. If a cloud appears, this changes
the frequency content, and new digital coefficients need to be generated and sent—or
perhaps not. We can choose to ignore the cloud, pretend it isn’t there, and send a “same
again” message, but some important information will have been left out.
A Q factor of 100 means any difference between pixel blocks is coded and sent. A Q
of 90 means small block-to-block differences are ignored with some (hardly noticeable)
loss of quality. A Q of 70 means larger block-to-block loss of quality, but it still is not
very noticeable. In digital cameras, a Q of 90 equates to fine camera mode, and a Q of
70 equates to standard camera mode. We choose 70 when we want to fit more pictures
into the memory stick or multimedia card. The choice of Q, however, also determines
delivery bandwidth requirements.
As mentioned, the noticeability of quality degradation is also a product of the quality
of display being used: A poor-quality display does not deserve a high Q picture; a
good quality display is wasted if a poor Q is used.
Say we have a picture taken in fine camera mode (Q = 90), which creates a file size
of 172,820 bytes. This will take 41.15 seconds to send over an uncoded 33.6 kbps channel
(this is assuming the user data rate is the same as the channel rate with no forward
error correction added in). If we took the same picture and had a Q of 5, the file size
would reduce to 12,095 bytes and we could send it at the same channel rate in 2.87 seconds.
The cost of delivery would be 15 times less for the Q-5 file. The question is, how
much would the quality be impaired and how much value would be lost.
This highlights an important issue. Voice-quality metrics are well established. We
use a mean opinion score to provide an objective way of comparing subjective quality
assessments. For instance, we put 10 people or 100 people in a room and ask them to
score a voice for quality, and then produce a mean opinion score (MOS) to describe the
perceived quality. JPEG Q gives us an objective measurement of image quality, but we
do not presently have a way of setting this against a subjective scorecard. As we will
see later, the same problem occurs with video quality.
This is important when we come to negotiate network quality with a customer. In a
2G cellular network, we agree with a network operator to a certain bit error rate (typically
1 in 103). This is deemed acceptable and defines the coverage area in which the
radio signal will be sufficient to deliver the defined BER or better. We can then show
how this BER relates to voice quality and define the MOS achievable across a percentage
of the coverage area.