An Integrated and Scalable Approach to Video Enhancement in ...

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Fig. 2: The histogram of the minimum intensity of each pixel’s three color channels of haze videos (Left), low lighting videos
(Middle) and high dynamic range videos (Right).
TABLE I: Results of chi square tests
Data of chi square test Degrees of Freedom Chi square values
Haze videos and inverted low lighting videos 7 13.21
Haze videos and inverted high dynamic range videos 7 11.53
TABLE II: Parameter settings for the haze detection algorithm.
Color attribute Threshold range
S 0 ∼ 255 0 ∼ 130
V 0 ∼ 255 90 ∼ 240
A. Automatic Impairment Source Detection
The function of the automatic impairment source detection
module is to classify input video into normal video that does
not need to be processed, low lighting video (also include
high-dynamic range video) for which pixel-wise inversion is
performed first, or hazy video (also include video captured
in rainy and snowy weathers) which is processed by the core
enhancement module directly.
A flow diagram for this automatic detection system is shown
in Fig. 3. Our detection algorithm is based on the technique
introduced by R. Lim et al. [11]. To reduce complexity, we
only perform the detection for the first frame in a Group of
Pictures (GOP), coupled with scene changing detection. The
corresponding algorithm parameters are given in Table II. The
same test was conducted for each pixel in the frame. If the
percentage of hazy pixels in a picture is higher than 60%, we
designate the picture as a hazy picture. Similarly, if an image
is determined to be a hazy picture after inversion, it is a low
lighting image.
B. Video De-Hazing Based Core Enhancement
Similar to [8], in our experiments, we used a system in which
the core enhancement algorithm is an improved video dehazing
algorithm based on the image de-hazing algorithm of
[14].
As is the case in many other advanced haze-removal algorithms
such as [14], [15], [16], and [17], was also based on
aforementioned Koschmieder model in (2). The critical part
of all image de-hazing algorithms based on the Koschmieder
Fig. 3: Flow diagram of the impairment source detection
module.
model is to estimate A and t(x) from the recorded image
intensity I(x) so as to recover the J(x) from I(x).
Following [14], we estimate the medium transmission and
airlight using the Dark Channel method:
{
R c }
(y)
t(x) = 1 − ω min min
c∈{r,g,b} y∈Ω(x) A c , (3)
where ω = 0.8 and Ω(x) is a local 3 × 3 block centered at x.
In our experiments, the cpu-and-memory-costly soft matting
method proposed in [14] was not implemented in the baseline
system, but could be used as a post-processing step, e.g. if the
output from the baseline system is subsequently uploaded to
a high power server in the cloud.
To estimate airlight, we first note that the schemes in existing
image haze removal algorithms are usually not very robust.
Even very small changes to the airlight value might lead
to very large changes in the recovered images or video
frames. As a result, calculating the airlight frame-by-frame