Abstract: Internet use, intelligent communication tools, and social media have all become an integral part of our daily life as a result of rapid developments in information technology. However, this widespread use increases crimes committed in the digital environment. Therefore, digital forensics, dealing with various crimes committed in digital environment, has become an important research topic. It is in the research scope of digital forensics to investigate digital evidences such as computer, cell phone, hard disk, DVD, etc. and to report whether it contains any crime related elements. There are many software and hardware tools developed for use in the digital evidence acquisition process. Today, the most widely used digital evidence investigation tools are based on the principle of finding all the data taken place in digital evidence that is matched with specified criteria and presenting it to the investigator (e.g. text files, files starting with letter A, etc.). Then, digital forensics experts carry out data analysis to figure out whether these data are related to a potential crime. Examination of a 1 TB hard disk may take hours or even days, depending on the expertise and experience of the examiner. In addition, it depends on examiner’s experience, and may change overall result involving in different cases overlooked. In this study, a hash-based matching and digital evidence evaluation method is proposed, and it is aimed to automatically classify the evidence containing criminal elements, thereby shortening the time of the digital evidence examination process and preventing human errors.
Abstract: This paper integrates Octagon and Square Search
pattern (OCTSS) motion estimation algorithm into H.264/AVC
(Advanced Video Coding) video codec in Adaptive Group of Pictures
(AGOP) mode. AGOP structure is computed based on scene change
in the video sequence. Octagon and square search pattern block-based
motion estimation method is implemented in inter-prediction process
of H.264/AVC. Both these methods reduce bit rate and computational
complexity while maintaining the quality of the video sequence
respectively. Experiments are conducted for different types of video
sequence. The results substantially proved that the bit rate,
computation time and PSNR gain achieved by the proposed method
is better than the existing H.264/AVC with fixed GOP and AGOP.
With a marginal gain in quality of 0.28dB and average gain in bitrate
of 132.87kbps, the proposed method reduces the average computation
time by 27.31 minutes when compared to the existing state-of-art
H.264/AVC video codec.
Abstract: Mosaic refers to a technique that makes image by gathering lots of small materials in various colors. This paper presents an automatic algorithm that makes the photo-mosaic image using photos. The algorithm is composed of 4 steps: partition and feature extraction, block matching, redundancy removal and color adjustment. The input image is partitioned in the small block to extract feature. Each block is matched to find similar photo in database by comparing similarity with Euclidean difference between blocks. The intensity of the block is adjusted to enhance the similarity of image by replacing the value of light and darkness with that of relevant block. Further, the quality of image is improved by minimizing the redundancy of tiles in the adjacent blocks. Experimental results support that the proposed algorithm is excellent in quantitative analysis and qualitative analysis.
Abstract: This paper presents an adaptive motion estimator
that can be dynamically reconfigured by the best algorithm
depending on the variation of the video nature during the lifetime
of an application under running. The 4 Step Search (4SS) and the
Gradient Search (GS) algorithms are integrated in the estimator in
order to be used in the case of rapid and slow video sequences
respectively. The Full Search Block Matching (FSBM) algorithm
has been also integrated in order to be used in the case of the
video sequences which are not real time oriented.
In order to efficiently reduce the computational cost while
achieving better visual quality with low cost power, the proposed
motion estimator is based on a Variable Block Size (VBS) scheme
that uses only the 16x16, 16x8, 8x16 and 8x8 modes.
Experimental results show that the adaptive motion estimator
allows better results in term of Peak Signal to Noise Ratio
(PSNR), computational cost, FPGA occupied area, and dissipated
power relatively to the most popular variable block size schemes
presented in the literature.
Abstract: In this paper a new Joint Adaptive Block Matching
Search (JABMS) algorithm is proposed to generate motion vector
and search a best match macro block by classifying the motion vector
movement based on prediction error. Diamond Search (DS)
algorithm generates high estimation accuracy when motion vector is
small and Adaptive Rood Pattern Search (ARPS) algorithm can
handle large motion vector but is not very accurate. The proposed
JABMS algorithm which is capable of considering both small and
large motions gives improved estimation accuracy and the
computational cost is reduced by 15.2 times compared with
Exhaustive Search (ES) algorithm and is 1.3 times less compared
with Diamond search algorithm.
Abstract: Full search block matching algorithm is widely used for hardware implementation of motion estimators in video compression algorithms. In this paper we are proposing a new architecture, which consists of a 2D parallel processing unit and a 1D unit both working in parallel. The proposed architecture reduces both data access power and computational power which are the main causes of power consumption in integer motion estimation. It also completes the operations with nearly the same number of clock cycles as compared to a 2D systolic array architecture. In this work sum of absolute difference (SAD)-the most repeated operation in block matching, is calculated in two steps. The first step is to calculate the SAD for alternate rows by a 2D parallel unit. If the SAD calculated by the parallel unit is less than the stored minimum SAD, the SAD of the remaining rows is calculated by the 1D unit. Early termination, which stops avoidable computations has been achieved with the help of alternate rows method proposed in this paper and by finding a low initial SAD value based on motion vector prediction. Data reuse has been applied to the reference blocks in the same search area which significantly reduced the memory access.
Abstract: Duplicated region detection is a technical method to
expose copy-paste forgeries on digital images. Copy-paste is one
of the common types of forgeries to clone portion of an image
in order to conceal or duplicate special object. In this type of
forgery detection, extracting robust block feature and also high
time complexity of matching step are two main open problems.
This paper concentrates on computational time and proposes a local
block matching algorithm based on block clustering to enhance time
complexity. Time complexity of the proposed algorithm is formulated
and effects of two parameter, block size and number of cluster, on
efficiency of this algorithm are considered. The experimental results
and mathematical analysis demonstrate this algorithm is more costeffective
than lexicographically algorithms in time complexity issue
when the image is complex.
Abstract: In MPEG and H.26x standards, to eliminate the
temporal redundancy we use motion estimation. Given that the
motion estimation stage is very complex in terms of computational
effort, a hardware implementation on a re-configurable circuit is
crucial for the requirements of different real time multimedia
applications. In this paper, we present hardware architecture for
motion estimation based on "Full Search Block Matching" (FSBM)
algorithm. This architecture presents minimum latency, maximum
throughput, full utilization of hardware resources such as embedded
memory blocks, and combining both pipelining and parallel
processing techniques. Our design is described in VHDL language,
verified by simulation and implemented in a Stratix II
EP2S130F1020C4 FPGA circuit. The experiment result show that the
optimum operating clock frequency of the proposed design is 89MHz
which achieves 160M pixels/sec.
Abstract: Motion estimation is a key problem in video
processing and computer vision. Optical flow motion estimation can
achieve high estimation accuracy when motion vector is small.
Three-step search algorithm can handle large motion vector but not
very accurate. A joint algorithm was proposed in this paper to
achieve high estimation accuracy disregarding whether the motion
vector is small or large, and keep the computation cost much lower
than full search.