Page 49 - Video Coding for Mobile Communications Efficiency, Complexity, and Resilience

26 Chapter 2. Video Coding: Fundamentals

storage requirements and high decoding complexity. In the modi ed Hu)man
code [23] the less probable symbols (and their probabilities) are lumped into
a single symbol like ESCAPE. A symbol in this new ESCAPE category is
coded using the VLC codeword for ESCAPE followed by extra bits to identify
the actual symbol. Standard video codecs also use 2-D and 3-D versions of
the Hu6man code. For example, the H.263 standard (see Section 3.4) uses a
3-D Hu6man code where three di6erent symbols (LAST, RUN, LEVEL) are
lumped into a single symbol (EVENT) and then encoded using one VLC
codeword.
One disadvantage of the Hu6man code is that it can only assign integer-
length codewords. This usually leads to a suboptimal performance. For ex-
ample, in Table 2.4, the symbol a 3 was represented with a 3-bit codeword,
whereas its information content is only 2:32 bits. In fact, Hu6man code can
be optimal only if all the probabilities are integer powers of 1=2. An en-
tropy code that can overcome this limitation and approach the entropy of the
source is arithmetic coding [24]. In Hu6man coding there is a one-to-one
correspondence between the symbols and the codewords. In arithmetic coding,
however, a single variable-length codeword is assigned to a variable-length
block of symbols.

2.5.6 Performance Measures

When evaluating the performance of a video coding system, a number of
aspects need to be assessed and measured. One important aspect is the amount
of compression (C) achieved by the system. This can be measured in a number
of ways:

number of bits in original video
C = (unitless); (2.15)
number of bits in compressed video
number of bits in compressed video
C = (bits=pel); (2.16)
number of pels in original video
number of bits in compressed video
C = × frame rate (bits=s):
number of frames in original video
(2.17)

Another important aspect is the reconstruction quality. This can be assessed
using a number of subjective and objective measures. Subjective measures are
normally evaluated by showing the reconstructed video to a group of subjects
and asking for their views on the perceived quality. A number of subjective
assessment methodologies have been developed over the years. Examples are