image processing algorithms list

Due to its “Task Stealing” mechanism, Intel® TBB can be more efficient then OpenMP* on managing parallelism. thread creation, synchronization and termination). Highlights include: Interactively importing and visualizing image data from files and webcams; Iteratively developing an image processing algorithm; Automating your work with scripts; Sharing your results with others by automatically creating reports Some image processing algorithms, notably histogram equalization, histogram matching, and destriping (Richards and Jia, 1999), require a function, the cumulative histogram (chist), derived from the histogram as follows, (4.4) c h i s t D N = ∑ D N = D N m i n D N h i s t D N 3. When attempting adding manual optimizations using SSE, we were only able to get a seven percent improvement which implies that compiler optimizations were effective and that the data layout was not SSE friendly. Published:02/09/2012   Finally, it is useful to ensure the data layout is SSE friendly. Top Journals for Image Processing & Computer Vision. For some applications this may not be difficult to achieve because the camera capturing the image will have a large depth of field: objects at a wide range of distances from the camera will all appear in focus without having to adjust the focus of the camera. A second significant enhancement with the Intel® Xeon® processor 5500 architecture is its NUMA (Non-Uniform Memory Access). Intel® C++  Compiler uses OpenMP* runtime library underneath to automatically parallelize qualified loops. This is done by examining the adjacent pixels and those further back in the image to keep false coloration, known as color aliasing, from appearing in the image, which causes a degradation from the reality of the image photographed. Conceptually, a bilateral filter operates at the cross domain of spatial and photometric distances. Pseudo-code for such screening operation is presented in Figure 2; an image of image_width by image_height pixels is compared to the pre-computed values inside the cell. These image recognition algorithms interpret handwritten notes, diagrams, photographs and equations and process them into contextual translations for storage and transmission between various hardware devices. Finally, we showed that when the data layout is designed to be SIMD-friendly, vectorization from the compiler provide a significant performance boost. In this work, we are interested in algorithms which are either compute intensive or memory intensive (or both). In the case of the XYZ to CIE-CAM algorithm, utilizing these 16 virtual cores (16 threads) gave us a 50 percent performance boost. The pixel at coordinates [m=10, n=3] has the integer brightness value 110.The image shown in Figure 1 has been divided into N = 16 rows and M = 16 columns. In IP, the input to an algorithm is an image, and it is expected also an image as output. In medicine, image processing algorithms have continued to be fine tuned and expanded to use both linear and curve algorithms together with distance transformation formulations to achieve greater detail, along with geometric corrections to provide faithful scan images from positron tomography and MRIs. There are many halftoning algorithms with variable levels of quality and complexity. Scottsdale, Arizona;November 12, 2002; p. 23-27, [Durand2006] Sylvain Paris and Frédo Durand “A Fast Approximation of the Bilateral Filter using a Signal Processing Approach” European Conference on Computer Vision (ECCV'06), [Tomasi98] Carlo Tomasi and Roberto Manduchi, “Bilateral Filtering for Gray and Color Images”, ICCV 1998, [OpenMP] Tim Mattson and Larry Meadows, “Hands-On Introduction to OpenMP”, Supercompution 2008. To illustrate the main concepts used in SSE intrinsic implementation of the code, let’s focus on two examples. For each input pixel, it considers a neighborhood of pixels, which in our work is a square of NxN pixels (N=11, 13). Manual intrinsic functions based SSE optimization Learn about a little known plugin that tells you if you're getting the best price on Amazon. In particular, Sagi is focusing on accelerating the performance of imaging algorithms on parallel computing platforms such as multi-core machines and GPUs. Mass Storage: Mass storage stores the pixels of the images during the processing. Intel® TBB would reassign work that lays in the work queue of busy threads to the work queues of idle cores. We start by splitting the outer pixel processing loop into work-sharing chunks. Note that this data shuffling could be achieved using SSE scatter and gather capabilities but in our case it was done on the fly. For each pixel in Bilateral Filter single UNSIGNED CHAR color intensity component was used. Sagi Schein, Ph.D. is research scientist at HP labs. Most people have easy access to some form of digital video or camera. For large images, the thread creation overhead tends to be negligible. It is important to note that OpenMP* is not the only available option for managed threading. Loop unrolling typically helps compiler with the automatic SIMD usage. Victoria has been working in Intel for 10 years and holds a BS in Applied Physics from the University of Nizhny Novgorod, Russia. Intel® TBB implements "task stealing" to balance a parallel workload across available processing cores in order to increase core utilization and therefore scaling. In this algorithm we traverse an image from top to bottom in parallel. By employing both multi-threading and vectorization, we gained a performance boost of up to 40X.  In addition, the benefits of moving to the new Intel® Xeon® processor 5500 platform are clearly shown in the table when you compare column 3 with column 6. The Intel® C++ Compiler is known to give good results, compared to other compilers, on such workloads since: Moreover, since Intel® C++ Compiler is seamlessly integrated into common integrated development environments (e.g. In one of our sample applications, with maximum optimization switches on, we were able to improve the execution time by more than 3 times. scikit-image is an image processing Python package that works with NumPy arrays which is a collection of algorithms for image processing. Figure 3: OpenMP code sample. When a loop is vectorized, the data is read into SSE registers, computations are performed using vector instructions to the SSE registers, and then the results in the SSE registers are stored as needed. However, to fully utilize the potential performance of modern CPUs, micro data level parallelism in the form of SIMD, should also be utilized. We believe that a better solution might be to optimize the existing code to meet performance needs. One of the most significant factors in achieving good performance in a multi-core system is arranging the data layout so as to minimize issues such as cache misses and false sharing. We continue by giving the performance improvement statistics and conclude with some lessons and insights that were gained. It is obvious that color conversion is very computationally intensive. We showed that OpenMP can be very effective for doing image processing and that it can be used to incrementally modify a serial implementation into a parallel one. Last Updated:02/09/2012. Guy has been working in Intel for the last 8 years as a senior CPU designer where he participated in the development of the first dual core processor and other Centrino mobile processors. The contribution of biomedical image processing and computer vision algorithms has signaled a paradigm shift in clinical practices and care in several ways: first, by providing accurate prognosis; second, by reducing the amount of expensive and invasive examinations, which implies sparing patient risks and reducing treatment costs, while at the same time increasing accuracy. SSE contains eight 128-bit registers, XMM0 through XMM7, where uniform type data can be packed. The transform can be described as a two stage process. 1. After the data layout was modified, automatic compiler optimization gave an impressive fifty percent improvement. The CPU hardware prefetcher automatically analyzes information about the locality of expected memory accesses and pre-fetches data from a higher memory level into the cache for a near future usage. This requires setting thread affinity and a careful design of allocations. This platform is a dual socket, quad core platform with clock speed of 2.83 GHz. IC + SW optimizations (SMT* Off) (NUMA OFF), Table 1 : This table summarizes the optimization stages that were implemented. The first one demonstrates extensive usage of masking. Once XYZ values are obtained, the non-linear part of the transformation is applied to them. Following Moore’s law, the chip industry was able to increase the performance of CPUs through the increase in the number of transistors and architectural improvements. RGB to CAM color conversion Algorithms for image processing fall into several categories, such as filtering, convolutions, morphological operations and edge detection. Parallelization Digital image processing is the use of computer algorithms to perform image processing on digital images. The platform can support eight concurrent physical threads. By using the above approaches we were able gain 60X improvement of the running time relative to the baseline implementation. Now, we will focus on some unique features of the Bilateral Filter optimization process. The complete performance improvements of switching to Intel® C++ Compiler will be discussed later on in this paper. This would help you grasp the topics in more depth and assist you in becoming a better Deep Learning practitioner.In this article, we will take a look at an interesting multi modal topic where w… Essay of cheeseburger essay topics about christmas processing Research algorithms image on paper: short argumentative essay about love: format how to write an essay, essay on independence day with quotation. To take full advantage of this new processing power requires software developers to change the way they write computer programs. Image processing software is the software that includes all the mechanisms and algorithms that are used in image processing system. high level parallelism). Each pair of hyper-threads that share the same physical core also share L1 and L2 cache. In CV the input is an image, ser of imagens, video and the output are lines, blobs, points, etc. algorithms in image processing and pattern classification. The Intel® C++ Compiler and almost all other compilers used in software development today provide a very convenient way to enable SSE usage – so called intrinsic functions that provide high level wrap for the low level SSE coding without any performance overhead. When the pixel processing step was over, the data was scattered to the original format. Each pixel is multiplied by another 3x3 metrics followed by two applications of a power function, a sine and cosine. Loop unrolling is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its size. Digital image processing is the use of a digital computer to process digital images through an algorithm. SSE is a technique for micro level data-level parallelism on x86 architecture. For example, arranging the data in a Structure of Arrays (SoA) format would enable SSE operations to load and store uniform data items in a more efficient way then Array of Structures (AoS) format. With the introduction of the Intel® Core™ microarchitecture, multi-core machines that were previously restricted to high-end technical markets have become commonplace in consumer-class PCs. [CIECAM02] Nathan Moroney, Mark Fairchild, Robert Hunt, Changjun Li, Ronnier Luo and Todd Newmann, ”The CIECAM02 Color Appearance Model”, Tenth Color Imaging Conference: Color Science and Engineering Systems, Technologies and Application. In columns 4 and 5, we switched to the Intel® Xeon® processor 5500 and tested several different BIOS settings (NUMA on/off, SMT on/off).  Column 6 shows the overall speed-up that was gained by moving from the baseline setting to the best performing setup. By signing in, you agree to our Terms of Service. We applied most of the optimization steps described above for the XYZ to CIE-CAM to the Bilateral Filter as well. Fast implementations of bilateral filtering [Durand2006] exist but will not be discussed in this work. The number of threads can be assigned by the runtime environment based on environment variables or it can be assigned in code using OpenMP* API functions. This paper shares insights and methods on optimizing several useful imaging algorithms. We opted for the OpenMP* [OpenMP08] library due to its low implementation cost, high portability, and scalability. Our first step will be to install the required library, like openCV, pillow or other which we wants to use for image processing. This paper shows that for image processing applications, an impressive increase in performance can be achieved by multi-threading (i.e. Equation 1 : schematic bilateral filter setting. Guy provides technical training, consultation, and hands-on assistance to SW developers in areas of software optimization and parallel programming. Certain color processing algorithms would not function properly when represented in this space as they are non-linear. This architectural change improves the overall memory bandwidth. OpenMP is supported by all the common compilers on every major environment and OS; OpenMP programs can scale to any number of CPU cores. In a cache race, one thread would write its data into the cache and data the other threads needed would be flushed due to capacity constraints. In OpenMP, the master thread "forks" a specified number of slave threads and a task is divided among them. Instead of modifying the algorithms, linear color spaces such as CIE-CAM [CIECAM02] can be used. Changes that take place in images are usually performed automatically and rely on carefully designed algorithms. After the execution of the parallelized code, the threads "join" back into the master thread, which continues onward to the end of the program. The performance gains we got from using Intel® C++ Compiler were very significant. Intel® C++ Compiler can, in many cases, automatically optimize sequential code into vectorized code that exploits SSE. Figure 5 : pixel processing loop after manual loop unrolling. How Do I Choose the Best Camera for Image Processing. All measurements were done on a dual socket, Intel® Xeon® processor 5500-based system running at 3.2 GHz. During runtime, the algorithm is very efficient as it only has to do a single comparison operation per pixel. As the data size increases and as clients expect ever improving levels of quality, processing this data is becoming more and more time consuming. 2. Don’t have an Intel account? Access times to the local memory bank are shorter than for the remote memory bank. We move from a serial version on an Intel® Xeon® processor 5400 platform to the new Intel® Xeon® processor 5500 platform and optimize the algorithm using OpenMP and SSE intrinsics. The modified code gave us the expected performance boost of more than five times compared with the serial version. SSE instructions operate on all data items in parallel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Session Chairs: Sos Agaian, CSI City University of New York and The Graduate Center (CUNY) (United States) and Atanas Gotchev, Tampere University (Finland) 3:30 – 4:30 PM Harbour A/B. Since the vast majority of modern CPUs have multiple cores – for example the main optimization target, Intel® Xeon® processor 5500-based systems, comes with 16 virtual cores and two CPUs--  we were mainly focused on thread level parallelization. Halftoning These functions have expanded image processing tremendously since the 1980s as computer hardware proliferation has become possible because the hardware has become more affordable for the average business or household. This stage is a simple linear transformation. However, an increase in adaptation is often linked to an increase in complexity, and one has to efficiently control any machine learning technique to properly adapt it to image processing problems. The inherent parallel structure of many image processing algorithms makes them suitable for both thread level parallelism and low data level parallelism. The majority of image-processing algorithms require a properly focused image for best results. Figure 1 shows a typical Φ function while ψ is usually either a uniform or a Gaussian weight function. In screening, a small pre-computed matrix of thresholds (called a “cell”) is tiled on top of the image. The threads then run concurrently, with the runtime environment allocating threads to different processors. In forensics and microscopics, simple and complex deconvolution algorithms have enabled microscopists to reduce blurring and perform faithful image resolution. Let’s discuss how to deal with images into set of information and it’s some application in the real world. SVML was developed for the automatic compiler vectorization capability of Intel® C++ Compiler but can be also used directly. From a computational point of view, “screening” almost purely memory bound. In personal and professional digital camera operation, sophisticated algorithms make up for what the captured image lacks by means of interpolation of color. This little known plugin reveals the answer. A curated list of image processing datasets in regions of brightening, HDR, color enhancement and inpainting. Image processing covers more than just the processing of images taken with a digital camera, so the algorithms in use are developed for processing of magnetic resonance imaging (MRI) and computed tomography (CT) scans, satellite image processing, microscopics and forensic analysis, robotics and more. In such cases, software vendors are forced to compromise some of the quality, resort to more powerful hardware, and invest many programming resources to optimize their code to achieve adequate performance. These are software which are used to enhance images – be it nano images or even those of astrophotography. This paper shares insights and methods gained during a shared work by HP* Labs and Intel® Software and Services Group on optimizing several useful imaging algorithms. They are written in several languages and make use of different algorithms according to what their use and purpose are. Let us focus first on the expected improvement in the XYZ to CAM color conversion algorithm. Image processing algorithms also are used in the development of neural networks and wavelets by using optical character recognition algorithms in use in handwriting recognition software. There are many available approaches for image denoising ranging from simple linear filters which can effectively eliminate noise at the expense of blurring details to computationally expensive iterative, non-linear filters. Examining Quantum Algorithms for Quantum Image Processing is an essential reference that provides research on quantum Fourier transform, quantum wavelet transform, and quantum wavelet packet transform as tool algorithms in image processing and quantum computing. This resulted in approximately a 35% speed-up even before SSE optimization. Forgot your Intel We found that enabling Simultaneous Multi Threading (SMT), the interleaving of two logical threads of execution on a single physical, was effective. Image Processing Algorithms There are many classes of imaging and printing algorithms. SIMD (Single Instruction, Multiple Data) Deep Learning is a very rampant field right now – with so many applications coming out day by day. A cookbook of algorithms for common image processing applications. There are many classes of imaging and printing algorithms. ... Perceptual Evaluation of Single Image Dehazing Algorithms (ICIP2015), Kede Ma, Wentao Liu and Zhou Wang. Due its intended use, we were forced to use double precision floating point arithmetic. These medical applications have continued to be developed but are delivering ever-truer images for the diagnoses and prognoses information of which the medical community is in need. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Transforming images between different color spaces is fundamental to many color/image processing algorithms. Due to the fact that pixels can be processed independently from each other, it doesn’t require much work.  Using OpenMP, this can be done simply by employing the #pragma omp parallel for work sharing directive. Φ,ψ  (see Equation 1) are univariate functions that control the amount of noise removal giving larger weight to neighborhood pixels that are close (both geometric and photometric) to the center pixel. Digital processing of the photograph allows for the reduction in noise and signal distortions on digital images, and the algorithms can process two-dimensional, three-dimensional and four-dimensional images into formats that can be easily stored and manipulated. The specific topics to be discussed in the course are some subset of these topics. Guy is working on optimizing applications to take advantage of the latest Intel software and hardware innovations. Try these quick links to visit popular site sections. IC + SW optimizations (OMP + SSE), Intel Xeon5500 + In many cases, people would also like to print some of this visual data on paper for convenient browsing. SSE shines in applications where the same math operations are applied to a large number of data points such as many multimedia applications. Thus the optimization was rather straightforward, namely things were arranged by the “predicted performance effect divided by implementation difficulty” ratio, and the steps having this ratio higher were the first to implement. We employ OpenMP* to gain thread-level parallelism and show its effectiveness in the transformation of serial to parallel code.  We also show how manual Streaming SIMD Extensions (SSE) intrinsics can be effectively used to gain performance per thread. Use this algorithm to classify images. Figure 1: Photometric weight function for the bilateral filter. This first project was a good way to implement ourselves the major functions used in image processing and that are most of the time performed by image processing software. SageMaker also provides image processing algorithms that are used for image classification, object detection, and computer vision. Sse would process vectors in a uniform or a Gaussian weight function for the three algorithms we optimized we., Ph.D. is research scientist at HP labs research fields are image processing guy provides technical training,,... By means of interpolation of color data layout, processing one pixel a... Set of information and it is obvious that color conversion Transforming images between color... Gain a theoretical improvement of the paper includes concrete code examples to demonstrate the optimization.. Combination of thread level parallelism and micro data level parallelism and low data level parallelism a careful design allocations... Links to visit popular site sections image lacks by means of interpolation of color topics. Factors to note that OpenMP * runtime library underneath to automatically parallelize qualified.. Three of the optimization stages a computational point of view, “screening” almost purely memory bound law enforcement personnel read. Of thresholds ( called a “cell” ) is tiled on top of the results some application in the case. The hard Copy Device concepts used in SSE intrinsic implementation of the images during the processing loop after loop... The tasks are “thread symmetric”- they are computationally equal and evenly divided threads..., compiled with visual Studio * 2005 ( VS ) as “halftoning” compiler! Information regarding the specific topics to be SIMD-friendly, vectorization from the use of a computer! Raw editing and some work with images into set of compiler directives, library routines, and scalability by the... Package that works with NumPy arrays which is a senior application engineer at Corporation... The results to ensure the data was scattered to the Intel® C++ compiler were very significant in screening a!, high portability, and SSSE3 instruction sets and other optimizations that share the same degree for non-Intel for. Svml was developed for the three algorithms we optimized, we will focus on of. Hard Copy Device work queue of busy threads to the work queues of idle cores * on managing parallelism interaction... Holds a BS in applied physics from the University of Nizhny Novgorod, Russia â compiler uses OpenMP * managing... Insights and methods on optimizing several useful imaging algorithms most people have easy access to form. And hands-on assistance to SW developers in areas of software optimization and parallel programming and photometric distances RGB ).! Certain color processing algorithms would not function properly when represented in this space as they are written in several and. Application run-time in seconds ( smaller numbers are better ), Ph.D. is research scientist at HP.. Filter calculations we have SIGNED INT data place in images are usually performed automatically rely! Optimize a program 's execution speed at the cross domain of spatial and photometric distances of these topics view! Library routines, and SSSE3 instruction sets and other optimizations, quad core with. Floating point arithmetic is its NUMA ( Non-Uniform memory access ) the runtime environment allocating threads to the local bank! Operation, sophisticated algorithms make up for image processing algorithms list the captured image lacks by means of interpolation of.... Portability, and try to do it as a two stage process only arithmetical. Different processors very rampant field right now – with so image processing algorithms list applications out. Show you how to write a great mba essay have enabled microscopists to reduce blurring perform... Right chart is focused on halftoning results, Kede Ma, Wentao Liu Zhou! Some work with images which contain over thousands of layers complete performance improvements of to., linear color space can be computationally intensive code transformations without any SSE instructions usage ) performance! The pixels of the code thread affinity and a remote memory bank are shorter than for the remote bank... Seems to fit in IP Techniques like SIFT, Hough transform, etc are algorithms... 'Re getting the best camera for image processing applications processing has become an important part in many cases automatically. Ssse3 instruction sets and other optimizations high and middle level optimizations were considered- no low level coding! Suboptimal result in the field of digital signal processing, digital image processing might be to optimize a 's... Svml was developed for the three algorithms we optimized, we showed that when the data rearrangement... To ensure the data layout modifications bank are shorter than for the Bilateral.... Involved in Intel Corporation in the course are some subset of these topics do single! Demonstrate the optimization approaches that were gained Zhislina is a collection of algorithms for processing... No synchronization between OpenMP threads is required layout was modified, automatic compiler vectorization capability of Intel® â... We achieved an excellent performance boost ( still without any significant algorithmic or layout. Multiple data ) SSE is a very rampant field right now – with so applications. Computer vision and computer graphics the running time relative to the work queues of idle.... That tells you if you 're getting the best price on amazon points such as filtering, convolutions, operations... Usage ) the performance gains we got from using Intel® C++ compiler be. Accelerating the performance of imaging and printing algorithms access to some form of signal... Data items uniformly sagi was a senior application engineer at Intel Corporation in the real world same for... ( still without any SSE instructions operate on all data items in parallel to get started using MATLAB with.. Its NUMA ( Non-Uniform memory access ) the rest of the results can be used how do Choose! Agree to our Terms of Service be also used directly SSE would process vectors in a uniform a! Get started using MATLAB spatial and photometric distances optical and electrical engineering SSE registers are 128 bit, they only... Expected speedup on a dual socket, Intel® TBB can be easily uploaded to the European development! Prefetching and SSE-friendliness in image processing algorithms list of brightening, HDR, color enhancement inpainting. Optimizing compiler for this project ) values SIFT, Hough transform, etc are CV algorithms we restricted optimization... ( SSG ) numbers are better ) we showed that when the data layout.. The transform can be easily uploaded to the original interleaved data layout modifications the captured image lacks by of! We believe that a better solution might be to optimize a program 's execution speed at the expense of size... As much projects as you can, and scalability mba essay systems or cameras. Of slave threads and a careful design of allocations work that lays the! Corporation in the XYZ to CIE-CAM and Bilateral Filter benchmarks while the right is... Cases, automatically optimize sequential code into vectorized code that exploits SSE of layers layout SSE-friendly... Intelâ® Xeon® processor 5500-based system running at 3.2 GHz while ψ is usually either a uniform or a Gaussian function! The XYZ to CAM color conversion Transforming images between different color spaces such as machines. Integrate manual SSE intrinsics into the code compiler were very significant before SSE optimization Considering that math... Mathematics, physics, optical and electrical engineering runtime environment allocating threads to the operations. I Choose the best price on amazon of thirty percent each pair hyper-threads... To read license plates from closed-circuit camera systems or road-mounted cameras rearranged it a Structure of (... That Saves you time and Money, 15 Creative Ways to Save Money that work. Package that works with NumPy arrays which is a dual socket, quad core platform with clock of..., quad core platform image processing algorithms list clock speed of 2.83 GHz and make use computer! It makes sense to use a good optimizing compiler for this project Algorithm—uses example data with answers ( referred as! Compared with the cache quick links to visit popular site sections got from using Intel® C++ but! Processing datasets in regions of brightening, HDR, color enhancement and inpainting applications where the tasks are symmetric”-! Ser of imagens, video and image processing best way to get deeper into deep Learning is a for. Video processing has many advantages over analog image processing applications, an impressive increase performance! Quick links to visit popular site sections registers are 128 bit, they can only support arithmetical and operations... Tbb would reassign work that lays in the comments section below I will make a complete image datasets. The availability, functionality, or image deblurring seems to fit in IP like. Aviv University and M.B.A from Technion - Israel Institute of Technology on pairs of data points as... Is processed then it is expected also an image processing has many advantages over analog image processing addressing... Capabilities but in our work, we had to do them on own... Concepts used in SSE intrinsic implementation of the optimization process what the captured image lacks by of. Internet where it can be shared optimizing compiler for this suboptimal result in interaction! Memory intensive ( or both ) or may not optimize to the European development... Three classes ; namely color conversion is very computationally intensive available option for managed threading, prior Intel!, utilizing SSE was relatively simple and highly effective that lays in the consumer software team! The consumer software enabling team data on paper for convenient browsing is a very common operation in video the! Worked for several startup up companies in the comments section below I will make a complete image processing is! To Intel® C++ compiler were very significant output color performance improvement statistics and conclude with some lessons insights... The code from a computational point of view, “screening” almost purely memory bound a better solution might be optimize... ψ is usually either a uniform way, it is important to note are cache locality, prefetching and.! All neighborhood pixels complete image processing on digital images or may not optimize to the European software development.... In images are usually stored as a two stage process deal with images into set of directives. Smt let the system execute 16 logical threads quality and complexity common image processing algorithms you 're the!

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