21 research outputs found
On the Simulation and Mitigation of Anisoplanatic Optical Turbulence for Long Range Imaging
We describe a numerical wave propagation method for simulating long range imaging of an extended scene under anisoplanatic conditions. Our approach computes an array of point spread functions (PSFs) for a 2D grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. To validate the simulation we compare simulated outputs with the theoretical anisoplanatic tilt correlation and differential tilt variance. This is in addition to comparing the long- and short-exposure PSFs, and isoplanatic angle. Our validation analysis shows an excellent match between the simulation statistics and the theoretical predictions. The simulation tool is also used here to quantitatively evaluate a recently proposed block- matching and Wiener filtering (BMWF) method for turbulence mitigation. In this method block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames are then averaged and processed with a Wiener filter for restoration. A novel aspect of the proposed BMWF method is that the PSF model used for restoration takes into account the level of geometric correction achieved during image registration. This way, the Wiener filter is able fully exploit the reduced blurring achieved by registration. The BMWF method is relatively simple computationally, and yet, has excellent performance in comparison to state-of-the-art benchmark methods
Real-time moving object segmentation in H.264 compressed domain based on approximate reasoning
AbstractThis paper presents a real-time segmentation algorithm to obtain moving objects from the H.264 compressed domain. The proposed segmentation works with very little information and is based on two features of the H.264 compressed video: motion vectors associated to the macroblocks and decision modes. The algorithm uses fuzzy logic and allows to describe position, velocity and size of the detected regions in a comprehensive way, so the proposed approach works with low level information but manages highly comprehensive linguistic concepts. The performance of the algorithm is improved using dynamic design of fuzzy sets that avoids merge and split problems. Experimental results for several traffic scenes demonstrate the real-time performance and the encouraging results in diverse situations
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Block matching (BM) motion estimation plays a very important role in video
coding. In a BM approach, image frames in a video sequence are divided into
blocks. For each block in the current frame, the best matching block is
identified inside a region of the previous frame, aiming to minimize the sum of
absolute differences (SAD). Unfortunately, the SAD evaluation is
computationally expensive and represents the most consuming operation in the BM
process. Therefore, BM motion estimation can be approached as an optimization
problem, where the goal is to find the best matching block within a search
space. The simplest available BM method is the full search algorithm (FSA)
which finds the most accurate motion vector through an exhaustive computation
of SAD values for all elements of the search window. Recently, several fast BM
algorithms have been proposed to reduce the number of SAD operations by
calculating only a fixed subset of search locations at the price of poor
accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC)
optimization is proposed to reduce the number of search locations in the BM
process. In our algorithm, the computation of search locations is drastically
reduced by considering a fitness calculation strategy which indicates when it
is feasible to calculate or only estimate new search locations. Since the
proposed algorithm does not consider any fixed search pattern or any other
movement assumption as most of other BM approaches do, a high probability for
finding the true minimum (accurate motion vector) is expected. Conducted
simulations show that the proposed method achieves the best balance over other
fast BM algorithms, in terms of both estimation accuracy and computational
cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with
arXiv:1405.4721, arXiv:1406.448
Block matching algorithm based on Harmony Search optimization for motion estimation
Motion estimation is one of the major problems in developing video coding
applications. Among all motion estimation approaches, Block-matching (BM)
algorithms are the most popular methods due to their effectiveness and
simplicity for both software and hardware implementations. A BM approach
assumes that the movement of pixels within a defined region of the current
frame can be modeled as a translation of pixels contained in the previous
frame. In this procedure, the motion vector is obtained by minimizing a certain
matching metric that is produced for the current frame over a determined search
window from the previous frame. Unfortunately, the evaluation of such matching
measurement is computationally expensive and represents the most consuming
operation in the BM process. Therefore, BM motion estimation can be viewed as
an optimization problem whose goal is to find the best-matching block within a
search space. The simplest available BM method is the Full Search Algorithm
(FSA) which finds the most accurate motion vector through an exhaustive
computation of all the elements of the search space. Recently, several fast BM
algorithms have been proposed to reduce the search positions by calculating
only a fixed subset of motion vectors despite lowering its accuracy. On the
other hand, the Harmony Search (HS) algorithm is a population-based
optimization method that is inspired by the music improvisation process in
which a musician searches for harmony and continues to polish the pitches to
obtain a better harmony. In this paper, a new BM algorithm that combines HS
with a fitness approximation model is proposed. The approach uses motion
vectors belonging to the search window as potential solutions. A fitness
function evaluates the matching quality of each motion vector candidate.Comment: 25 Pages. arXiv admin note: substantial text overlap with
arXiv:1405.472
Reconfigurable Architecture For H.264/avc Variable Block Size Motion Estimation Based On Motion Activity And Adaptive Search Range
Motion Estimation (ME) technique plays a key role in the video coding systems to achieve high compression ratios by removing temporal redundancies among video frames. Especially in the newest H.264/AVC video coding standard, ME engine demands large amount of computational capabilities due to its support for wide range of different block sizes for a given macroblock in order to increase accuracy in finding best matching block in the previous frames. We propose scalable architecture for H.264/AVC Variable Block Size (VBS) Motion Estimation with adaptive computing capability to support various search ranges, input video resolutions, and frame rates. Hardware architecture of the proposed ME consists of scalable Sum of Absolute Difference (SAD) arrays which can perform Full Search Block Matching Algorithm (FSBMA) for smaller 4x4 blocks. It is also shown that by predicting motion activity and adaptively adjusting the Search Range (SR) on the reconfigurable hardware platform, the computational cost of ME required for inter-frame encoding in H.264/AVC video coding standard can be reduced significantly. Dynamic Partial Reconfiguration is a unique feature of Field Programmable Gate Arrays (FPGAs) that makes best use of hardware resources and power by allowing adaptive algorithm to be implemented during run-time. We exploit this feature of FPGA to implement the proposed reconfigurable architecture of ME and maximize the architectural benefits through prediction of motion activities in the video sequences ,adaptation of SR during run-time, and fractional ME refinement. The implemented ME architecture can support real time applications at a maximum frequency of 90MHz with multiple reconfigurable regions. iv When compared to reconfiguration of complete design, partial reconfiguration process results in smaller bitstream size which allows FPGA to implement different configurations at higher speed. The proposed architecture has modular structure, regular data flow, and efficient memory organization with lower memory accesses. By increasing the number of active partial reconfigurable modules from one to four, there is a 4 fold increase in data re-use. Also, by introducing adaptive SR reduction algorithm at frame level, the computational load of ME is reduced significantly with only small degradation in PSNR (≤0.1dB)
A survey on video compression fast block matching algorithms
Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences.
Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50–80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called Full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity.
This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the Full Search is decreased while the resolution of the predicted frames is the same as for the Full Search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the Full Search