International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 2 – Oct 2014
FPGA Implementation of Increased Video
Quality Using Tone Adjustment
P Himabindu#1, R Padmaja*2
1,2
Khammaminstitute of technology and science, Dept., of ECE
Khammam, Telangana
Abstract:
The quality of video increased by the different enhancement techniques. In this paper we propose a video quality
increasing technique by using tone adjustment. The human eye is very sensitive and very fascinating. The better quality video is
proposed for human eyes. The quality of video cameras in ATMs, Shopping malls, and other heavy traffic areas is very poor. The
increase of video quality from these low quality video cameras will be benefitted for the human life applications. This technique is
implemented in Field programmable gate arrays vertex 5 family, and HSV transform algorithm is applied to the video frames
which finally gets the high quality video out. Implemented with verilog language.
Key words: FPGAS, Image processing, Tone adjustment, DWT
I.
Introduction
Video enhancement is one of the most important and
difficult components in video research. The aim of video
enhancement is to improve visual appearance of the video, or
to provide a better transform representation for future
automated video processing, such as analysis, detection,
segmentation, recognition, surveillance, traffic, criminal
justice systems [1]. Digital video has become an integral part
of everyday life. It is well-known that video enhancement as
an active topic in computer vision has received much
attention in recent years. Carrying out video enhancement
understanding under low quality video is a challenging
problem because of the following problems [1]. (1) Due to
low contrast, we cannot clearly extract moving objects from
the dark background. Most color-based methods will fail on
this matter if the color of the moving objects and that of the
background are similar. (2) The signal to noise ratio is
usually very low due to high ISO. Using a high ISO number
can produce visible noise in digital photos. Low ISO number
means less sensitivity to light. (3) The information carrying
video signal is a degraded version of a source or original
video signal which represents the three dimensional
continuous world. These degradations can be a result of the
acquisition process, or the rate and format conversion
processes. (4) Environmental information affects the way
people perceive and understand what has happened. Hence
dealing with moving tree, fog, rain, behavior of people in
night time video are the difficult because they lack
background context due to poor illumination. (5) Inter-frame
coherence must also be maintained i.e. the moving objects
region as weights in successive images should change
smoothly. (6) One pixel from a low quality image may be
important even if the local variance is small, such as the area
between the headlights and the taillights of a moving car. (7)
The poor quality of the used video device and lack of
ISSN: 2231-5381
expertise of the operator. Our paper concentrates on the point
(7) in which the input is the video from poor quality video
cameras.
The dynamic range of natural luminance intensity can reach
approximately 1010:1 while conventional printers or displays
only show images in dynamic range of 100-1000:1 [2]. It is
due to the limitation of bit resolution in the video format and
also in capturing devices. That inconsistency makes video
details disappear if the background is too dark or too bright.
Therefore, various video enhancement techniques are
introduced to retrieve the hidden details in video by
enhancing contrast and make video look closer to real scenes
[3][4]. The figure 1 shows the different enhancement video
techniques [1].
Figure 1 Different enhancement techniques
II.
Discrete wavelet transform
The two level sub-band representation of the DWT output is
shown in figure 2. The input image of size N*N is processed
by high pass and low pass filters as shown in the figure 3.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 2 – Oct 2014
Along rows and columns followed by sub sampling to
generate the first level outputs HL1, HH1, LH1 and LL1.
Each of size (N/2)*N/2). The LL1 further processed by high
pass and low pass filters along the rows and columns to
generate the second level outputs HH2, HL2, LH2 and LH1
contains co efficient with small values. Moreover, the co
efficient in a sub-band has similar magnitudes. We exploit
these facts in developing the memory error compensation
techniques.
Figure 2 sub-band representation of the DWT
Figure 4 Proposed frame work
Figure 3 DWT process
III.
Proposed video enhancement
The figure 4 represents the proposed frame work of the video
enhancement. Global contrast enhancement is required to
reveal hidden details in dark and bright regions. In addition
to enhancing regions with extremely high or low luminance,
proposed technique is also significantly stretches the contrast
in mid-tone regions, which most other curve-based global
enhancement methods ignore [4].
ISSN: 2231-5381
Saliency values can be regarded as complex local
information indicating the degree of human interest in each
pixel in a video. Saliency maps are most frequently used to
extract useful objects in the preprocessing of surveillance
systems or recognition problems. The saliency maps as a
reference for local contrast enhancement and proposes the
Saliency weighted contrast enhancement technique. First, the
videos are separated in to frames. These frames then
transformed to the discrete wavelet transform. The operation
of the discrete wavelet transform is discussed in the Section
discrete wavelet transform. Frames are transformed to HSV
(Hue Saturation Value) transform to derive the luminance
and color maps. Then, Bilateral tone adjustment is applied to
the luminance map, saliency detector calculates the saliency
map [4] [5]. Figure 4 shows the proposed video enhancement
technique. Basically all kinds of saliency detection methods
can be used for saliency map generation. This adopts color
saliency detection method because it produces a satisfactory
saliency map. SWCE is then performed based on the
adjusted luminance and saliency map. Finally, the output
image is the inverse HSV and DWT of the enhanced
luminance map with the original color maps [4]
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 2 – Oct 2014
IV.
Saliency-weighted contrast
enhancement
Due to the bilateral tone adjustment, we do not need to
gather and analyze global luminance information in this post
process of contrast enhancement. Due to the Bilateral tone
adjustment, we do not need to gather and analyze global
luminance information in this post process of contrast
enhancement. The SWCE is designed to produce high
contrast in regions with higher extent of human interests. In
addition, the noise is not over-enhanced because SWCE
controls the enhancement extent adaptively depending on
local saliency values. The proposed method reveals the
details contained in dark regions, and the enhancement result
is quite smooth and natural [6]. The SWCE is designed to
produce high contrast in regions with higher extent of human
interests. In addition, the noise is not over enhanced because
SWCE controls the enhancement extent adaptively
depending on local saliency values. The proposed method
reveals the details contained in dark regions, and the
enhancement result is quite smooth and natural.
Figure 6 RTL view
Table 1: Results
POWER USED
SPEED
APPROXIMATELY
MEMORY USAGE
VI.
V.
281 MW
2.9nS
117344 KB
Conclusion
Simulation results
The figure 5 shows the enhanced image frame output is from
mat lab code. Figure 6 shows the RTL view of the design.
The design is implemented in verilog and synthesized the
system in Xilinx ISE 13.2 version and vertex 5 FPGA
family. The power results have been shown in table 1. Speed
and memory usage results also shown in the table 1.
We have designed a system for video to increase its quality
and implemented it with fpga family. This technique is
useful to identify the low quality video images in traffic
areas, in ATMs etc. This technique can also increase the
video quality from the low surveillance cameras. We have
also shown the advantages by the implementation with field
programmable gate arrays.
References
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Yunbo Rao, Leiting Chen “A Survey of Video Enhancement
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43, no. I,pp. 1-8,Feb. 1997.
[3]
S. -D. Chen and A. R. Ramli, "Minimum mean brightness error
bihistogram equalization in contrast enhancement," IEEE Trans.
Consumer Electron., vol. 49, no. 4, pp. 1310-1319, Nov. 2003.
[4]
D.BakkiyaLakshmi , R.Kanchana and V. Nagarajan, “Video
Enhancement using Tone Adjustment” IEEE advancing
technology for humanity, 2012.
[5]
C. Wang, Q. Yang, X. Tang, and Z. Ye, "Salience preserving
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[6]
D.BakkiyaLakshmi , R.Kanchana and V. Nagarajan “Video
Enhancement using Tone Adjustment” IEEE 2012.
Figure 5 enhanced image frame output
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