1 MOTIVATION: Intensity changes
are observed frequently in
commercial videos
Video Coding with Linear Compensation (VCLC)
Arif Mahmood, Zartash Afzal Uzmi, Sohaib A Khan
{arifm,zartash,sohaib}@lums.edu.pk
Department of Computer Science, Lahore University of Management and Sciences, Lahore, PAKISTAN
4 SAD Fails as an Match Measure if Video
undergoes light changes
SAD is optimal from coding point of view in the absence of light
changes, since it minimizes the residue
Consecutive frames from movies and music videos: Blade II (2002), Batman
Begins (2005), Pink Floyd: Coming Back to Life (1994).
2 Exploitation of Temporal Redundancy
Results in Storage and Communication
Efficiency
Temporal Redundancy is exploited
through:
Motion Estimation: The process of
finding a matching block in the
temporally adjacent frame
Compensation: The process of
computing the signal for
communication or storage
3 CURRENTLY: Sum of Absolute Differences
(SAD) is used for Motion Estimation
ESTIMATION of location of matching block is through SAD
n
n
SAD   b(k , x, y )  b(k ' , x' , y ' )
x 1 y 1
COMPENSATION is done through computing the difference
at the match location
RESIDUE  b(k , x, y )  b(k ' , x' , y ' )
However, SAD is NON-OPTIMAL if brightness or contrast
changes are present in video
5 OUR PROPOSED SCHEME: Video Coding
Using Linear Compensation
Gives consideration to brightness and contrast changes in a
sequence of frames
 b(k , x, y)  b(k , x, y)b(k ' , x' , y' )  b(k ' , x' , y' )
n
x 1 y 1
 b ( k , x , y ) b ( k ', x ', y ')
COMPENSATION is done through computing the difference with
the linear estimate of block intensity
bˆ(k , x, y )  b(k ' , x' , y ' )  
α and β are selected by minimizing the Mean Squared Error and
are given by:
b
  bb'
;
 b'
b
  b  bb'
b '
 b'
Implemented a generic encoder, and measured the
PSNR of original and decoded signal at receiver
end
2
255
PSNR  10 log 10
MSE
The average and standard
deviation of Mean Squared Error
of different estimation filters.
More than 400,000 8x8 blocks
were taken to compute these
statistics
7 KEY THEORETICAL RESULTS
ESTIMATION of location of matching block is done through
correlation coefficient
 bb' 
Used a dataset of clips taken from commercial
videos
Brightness and Contrast
Changes are well
modeled by first order
linear model
Going to a higher order
model is a case of
diminishing returns:
Variance is not further
decreased by much, and
the cost, in terms of
number of parameters,
becomes larger
Example: If all pixels in a block become brighter by Δ, the location
where content matches will become brighter by nΔ, and may no
longer be the location with least residue
n
8 EXPERIMENTAL RESULTS
6 Why Linear Model?
RESULT 1: For the same motion estimator,  i.e.
the variance of the difference signal after linear
2

compensation, is upper bounded by  d (the
variance of the simple difference)
2
p
This means that from compression point of view, the
difference signal after linear compensation should
always be better, or equal, to the simple difference
scheme employed by current codecs
RESULT 2: When linear compensation is used, the
optimal criterion for motion estimation is
correlation coefficient, rather than SAD
We have proved that no other motion estimator can
give match at a location that results in lesser first
order linear compensated difference
Variation of PSNR with the variation of bits per pixel for
the VCLC scheme and the traditional generic encoder.
Red curves being higher indicate more optimal
compression