Optimization of Image Processing Algorithms: A Case Study

Published: 02/09/2012, Last Updated: 02/09/2012


High quality image and video processing has become an important part in many professional and consumer applications. Unfortunately, it often comes with a high performance price. 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. 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. It focuses on how the multi-core architecture of 5500 series Intel® Xeon® Processors combined with Intel’s Single Instruction Multiple Data (SIMD) technology can be exploited to achieve a significant performance boost. The paper includes concrete code examples to demonstrate the optimization process. We start from a serial non-optimized baseline algorithm and apply data layout modifications to both make the code more SIMD-friendly and to improve the compiler’s ability to vectorize it. 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. By using the above approaches we were able gain 60X improvement of the running time relative to the baseline implementation.


Visual data is becoming an important part of our digital life. Most people have easy access to some form of digital video or camera. This visual data can be easily uploaded to the internet where it can be shared. In many cases, people would also like to print some of this visual data on paper for convenient browsing. As the data size increases and as clients expect ever improving levels of quality, processing this data is becoming more and more time consuming. To maintain adequate performance, software vendors may compromise some of the quality or resort to more powerful and expensive hardware. We believe that a better solution might be to optimize the existing code to meet performance needs. This paper shares insights and methods on optimizing several useful imaging algorithms.

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. However, keeping the pace of improvement has become ever more difficult from one CPU generation to the next and CPU-designers have turned to multi-core solutions to meet the ever growing performance demands. 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. To take full advantage of this new processing power requires software developers to change the way they write computer programs. This paper shows that for image processing applications, an impressive increase in performance can be achieved by multi-threading (i.e. high level parallelism). However, to fully utilize the potential performance of modern CPUs, micro data level parallelism in the form of SIMD, should also be utilized. This work shows that the combination of thread level parallelism and micro data level parallelism can be highly effective.

The rest of the paper is organized as follows. We start by describing the algorithms that were optimized. Then, focusing on one of the algorithms, we describe the optimization approaches that were used. We continue by giving the performance improvement statistics and conclude with some lessons and insights that were gained.

Image Processing Algorithms

There are many classes of imaging and printing algorithms. In this work, we are interested in algorithms which are either compute intensive or memory intensive (or both). We focus on three classes; namely color conversion, filtering and halftoning and pick three sample algorithms.

1. RGB to CAM color conversion
Transforming images between different color spaces is fundamental to many color/image processing algorithms. For example, images are usually stored as a triplet of red, green and blue (RGB) values. Certain color processing algorithms would not function properly when represented in this space as they are non-linear. Instead of modifying the algorithms, linear color spaces such as CIE-CAM [CIECAM02] can be used. Unfortunately, transforming an image from RGB color space to linear color space can be computationally intensive.

The transform can be described as a two stage process.

  • First values are transformed from RGB color-space into XYZ color space. This stage is a simple linear transformation, where is a 3x3 transformation matrix from XYZ to RGB.
  • Once XYZ values are obtained, the non-linear part of the transformation is applied to them. Each pixel is multiplied by another 3x3 metrics followed by two applications of a power function, a sine and cosine.

The full description of RGB to CIE-CAM color transform can be found in [CIECAM02].  From a performance point of view, these color space transformations are point operations. This means that a single input color is transformed into a single output color. The memory requirements for the algorithm are optimal in the sense that each pixel is loaded once from memory and stored away once in its transformed value.

2. Bilateral Filter
Noise-removal is a very common operation in video and image processing. 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.

In this work, we consider bilateral filter [Tomasi98], a non-iterative, non-linear filter, which can yield good denoising while avoiding blurring of small details at an acceptable computational cost. For each input pixel, it considers a neighborhood of pixels, which in our work is a square of NxN pixels (N=11, 13). Conceptually, a bilateral filter operates at the cross domain of spatial and photometric distances. Each output pixel is a weighted average of all neighborhood pixels. However, different from linear filters, the pixel weights are computed as a function of the geometric as well as the photometric distance of the center pixel, pi, to each of its neighborhood pixels, pj. 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.

Equation 1 : schematic bilateral filter setting.

Figure 1 shows a typical Φ function while ψ is usually either a uniform or a Gaussian weight function.

Figure 1: Photometric weight function for the bilateral filter.

Bilateral filters are more computational expensive than linear filters. Whereas linear filters can be efficiently computed as a convolution of the image with a pre-computed kernel, in the bilateral filter a set of data-dependent weights is computed per pixel. From a computational point of view the algorithm requires the same number of memory accesses as a linear filter of the same support but more computations are needed per pixel. Fast implementations of bilateral filtering [Durand2006] exist but will not be discussed in this work.

3. Halftoning
Representing a continuous-tone image using only two colors is a fundamental digital printing operation, known as “halftoning”. There are many halftoning algorithms with variable levels of quality and complexity.

In our work, we focus on “screening” which is probably the simplest halftoning algorithm and is widely used in industrial printing. In screening, a small pre-computed matrix of thresholds (called a “cell”) is tiled on top of the image. Then, for each image pixel which is above its corresponding threshold, the algorithm returns one; otherwise it returns zero.  It is important to note that in order to reduce printing artifacts; care must be taken when designing cell values and selecting cell values is done offline. During runtime, the algorithm is very efficient as it only has to do a single comparison operation per pixel. 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. The typical dimensions of a cell can vary from 64 by 64 to 512 by 512, which is usually much smaller than the image size, hence the tiling operation on line (3) is required. From a computational point of view, “screening” almost purely memory bound.

	for i = 1:image_width
	    for j = 1:image_height
	         if cell[i % cell_width] [j % cell_height] > image[i][j] 
			   binary_image[i][j] = 1;
			   binary_image[i][j] = 0;

Figure 2: pseudo-code for a simple screening algorithm

Optimization description steps

In this section we focus on the optimization steps that were applied to the “XYZ to CIE-CAM” and Bilateral Filter algorithms. Only high and middle level optimizations were considered- no low level assembly coding. Moreover, we restricted the optimization to local code transformations without any significant algorithmic or data layout modifications. 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.

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. We opted for the OpenMP* [OpenMP08] library due to its low implementation cost, high portability, and scalability. OpenMP is supported by all the common compilers on every major environment and OS; OpenMP programs can scale to any number of CPU cores. OpenMP, consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior. In OpenMP, the master thread "forks" a specified number of slave threads and a task is divided among them. The threads then run concurrently, with the runtime environment allocating threads to different processors. The section of code that is meant to run in parallel is marked accordingly, with a preprocessor directive (see Figure 3) that will cause the threads to form before the section is executed. After the execution of the parallelized code, the threads "join" back into the master thread, which continues onward to the end of the program. "Work-sharing” constructs can be used to divide a task among the threads so that each thread executes its allocated part of the code.  The runtime environment allocates threads to processors depending on usage, machine load and other factors. 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.

  1. XYZ to CAM

We start by splitting the outer pixel processing loop into work-sharing chunks. 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. The iteration space of all image pixels will be traversed in parallel.

#pragma omp parallel for private(colin,colout)
for(int i = 0; i < image->width*image->height*3; i+=3)
    //for every pixel in the image, apply color transformation
    //colout from colin 

Figure 3: OpenMP code sample. In this algorithm we traverse an image from top to bottom in parallel.

Since each input pixel is mapped into a distinct output pixel, no synchronization between OpenMP threads is required. For large images, the thread creation overhead tends to be negligible. In such a case, the expected speedup on a quad-core (with Intel® Hyper-Threading Technology) should be above 4 times. Unfortunately, the real improvement turned out to be less than 3 times. After doing some analysis, we could pinpoint the reason for this suboptimal result in the interaction with the cache. Each pair of hyper-threads that share the same physical core also share L1 and L2 cache. In a system with Intel® quad-core technology all cores on the same physical core also share the L3 cache.  As a result, if we load small chunks of data from separate threads on the same physical core, we can create “cache races”.

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. The solution is to load chunks of data into cache once, change the thread granularity so each thread processes one image line, and have multiple threads process in the same chunk.   In OpenMP, this is done by setting the data schedule clause. Note: in our case each image line is much larger then a cache line, so we need not be concerned about false cache sharing (a situation where two threads access adjacent data within the same cache-line casing ping-pong effect). In the general case, this effect should be considered as well and could influence the thread data chunk. The modified code gave us the expected performance boost of more than five times compared with the serial version.

#pragma omp parallel for private (colin,colout) schedule(dynamic,3*image->width)
for(int i = 0; i < image->width*image->height*3; i+=3)
   //for every pixel get colout from colin, each thread processes an image line. 

It is important to note that OpenMP* is not the only available option for managed threading. For example, Intel® Threading Building Blocks [Intel® TBB] is a C++ runtime library that does thread management, letting developers focus on proven parallel patterns and take advantage of multi-core processors. Using Intel® TBB, the programmer can avoid some complications arising from the use of native threading packages (e.g. thread creation, synchronization and termination). In Intel® TBB, operations are treated as "tasks," which are allocated to individual cores dynamically by the library's run-time engine, and by automating efficient use of the cache. Intel® TBB implements "task stealing" to balance a parallel workload across available processing cores in order to increase core utilization and therefore scaling. Initially, the workload is evenly divided among the available processor cores. Intel® TBB would reassign work that lays in the work queue of busy threads to the work queues of idle cores. Due to its “Task Stealing” mechanism, Intel® TBB can be more efficient then OpenMP* on managing parallelism. However in our case where the tasks are “thread symmetric”- they are computationally equal and evenly divided between threads, OpenMP works fine.


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. Important factors to note are cache locality, prefetching and SSE-friendliness. 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. To reduce memory access cost, it is important to arrange the data in such a way that helps the prefetcher to correctly predict data access patterns. Finally, it is useful to ensure the data layout is SSE friendly. Since SSE would process vectors in a uniform way, it makes sense to group data items uniformly. 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.

Using Intel® C++ Compiler
It is obvious that color conversion is very computationally intensive. And that is why it makes sense to use a good optimizing compiler for this project. The Intel® C++ Compiler is known to give good results, compared to other compilers, on such workloads since:

  • Intel® C++ Compiler uses SVML (Short Vector Math Library) internally for auto SIMD vectorization.
  • Intel® C++  Compiler uses LibM Math Library internally for highly optimized scalar math functions as direct replacements for the standard C calls.
  • Intel® C++  Compiler uses OpenMP* runtime library underneath to automatically parallelize qualified loops.

Moreover, since Intel® C++ Compiler is seamlessly integrated into common integrated development environments (e.g. Microsoft* Visual Studio* on Windows*, Eclipse* on Linux*, XCode* on Mac OS* X), it is very easy to use. The performance gains we got from using Intel® C++ Compiler were very significant. In one of our sample applications, with maximum optimization switches on, we were able to improve the execution time by more than 3 times. The relevant switches used were /O3 (Maximize speed plus high level optimization) and /arch:SSSE3 (in this case SSE4.x were not used in the optimization manually).  In addition / Qopenmp (enable OpenMP) and /Oi (enable intrinsic functions) were used. The complete performance improvements of switching to Intel® C++ Compiler will be discussed later on in this paper.

SIMD (Single Instruction, Multiple Data)
SSE is a technique for micro level data-level parallelism on x86 architecture. SSE contains eight 128-bit registers, XMM0 through XMM7, where uniform type data can be packed. SSE instructions operate on all data items in parallel.

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. Intel® C++ Compiler can, in many cases, automatically optimize sequential code into vectorized code that exploits SSE. 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. SSE shines in applications where the same math operations are applied to a large number of data points such as many multimedia applications.

Manual intrinsic functions based SSE optimization
Considering that complicated math operations were needed in this case (e.g. power functions, sin, cosine etc), in addition to standard compiler intrinsic functions, we also found the Intel SVML library functions to be highly effective. SVML was developed for the automatic compiler vectorization capability of Intel® C++ Compiler but can be also used directly.

Let us focus first on the expected improvement in the XYZ to CAM color conversion algorithm. Due its intended use, we were forced to use double precision floating point arithmetic. Since SSE registers are 128 bit, they can only support arithmetical and logical operations on pairs of data units in parallel. On the original interleaved data layout, processing one pixel at a time, we could only gain a theoretical improvement of thirty percent. 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.

In order to make the data layout more SSE-friendly we rearranged it a Structure of Arrays (SoA) format. Pairs of pixels were shuffled so that the data is laid-out in a (R,R,G,G,B,B) setting. Optimally, this data preparation should be done offline. In our case, we had to do it as a first step to our pixel processing loop. When the pixel processing step was over, the data was scattered to the original format. Note that this data shuffling could be achieved using SSE scatter and gather capabilities but in our case it was done on the fly. After the data processing rearrangement (still without any SSE instructions usage) the performance improved significantly. Unfortunately, due to the complex pixel processing, the compiler was not able to unroll the processing loop.

Loop unrolling is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its size. Loop unrolling typically helps compiler with the automatic SIMD usage. Namely in our case we increase loop counter i (processing 3 color components at a time) by 2 (processing 2*3 color components at a time), repeating 3 components processing twice manually.  As can be seen in the example below, the “C” variable is divided into two operations “C[0]” and “C[1]” as we increase loop counter i by 2.  Transforming the code in Figure 4 to the code in Figure 5.

void Cam022Lab_f(COLOUR_FPTYPE* camin, COLOUR_FPTYPE* labout, int npixels)
for(int i = 0; i < npixels; i++){
lab = labout + i * 3;
jab = camin + i * 3; 

C = ab2C(jab[1], jab[2]);

Figure 4: original pixel processing loop

void Cam022Lab_f(COLOUR_FPTYPE* camin, COLOUR_FPTYPE* labout, int npixels)
for(int i = 0; i < npixels; i+=2){
	       lab = labout + i * 6;
       jab = camin + i * 6; 

C[0] = ab2C(jab[1], jab[2]);
       C[1] = ab2C(jab[4], jab[5]);


Figure 5 : pixel processing loop after manual loop unrolling

After the data layout was modified, automatic compiler optimization gave an impressive fifty percent improvement. From that point we started to integrate manual SSE intrinsics into the code. To illustrate the main concepts used in SSE intrinsic implementation of the code, let’s focus on two examples. The first one demonstrates extensive usage of masking.

#ifndef __USE_SSE
	xyzt[0][0] = xyz[0][0] < 0.0 ? 0.0 : xyz[0][0];
	xyzt[1][0] = xyz[0][1] < 0.0 ? 0.0 : xyz[0][1];
	xyztSSE = _mm_and_pd(xyztSSE, _mm_cmpgt_pd(xyztSSE, _mm_setzero_pd()) ); 

Here, we compare xyz[0][0,1] with 0, and if xyz[0][0,1] >0 we have "ones" in the mask otherwise we have "zeros", then anding xyz[0][0,1] with this mask gives us 0 or xyz[0][0,1]. The second example focuses on SVML but also demonstrates the use of masking.

#ifndef __USE_SSE
RGBp[0][0] = pow(((27.13 * abs(RGBpa[0][0] - 0.1)) / (400.0-abs(RGBpa[0][0] - 0.1))), (1.0/0.42))*G3; 
RGBp[0][0] = RGBpa[0][0] < 0.1 ? - RGBp[0][0] : RGBp[0][0];

RGBp[0][1] = pow(((27.13 * abs(RGBpa[0][1] - 0.1)) / (400.0-abs(RGBpa[0][1] - 0.1))), (1.0/0.42))*G3;  
RGBp[0][1] = RGBpa[0][1] < 0.1 ? - RGBp[0][1] : RGBp[0][1];
__declspec(align(16)) static const __int64 ffff[2]= {0x7fffffffffffffffL,0x7fffffffffffffffL} ;
 static __m128d const01 =_mm_set1_pd(0.1);
  rgbp[0] =  _mm_sub_pd(rgbp[0],const01); //(RGBpa[0][0] - 0.1),(RGBpa[0][1] - 0.1)
  temp1 =  _mm_andnot_pd(*(__m128d*)ffff,rgbp[0] ); //sign bit of (RGBpa[0,1] - 0.1)
  rgbp[0] =  _mm_and_pd(*(__m128d*)ffff,rgbp[0] ); //abs(RGBpa[0,1] - 0.1)

 rgbp[0] =  __svml_pow2(_mm_div_pd(_mm_mul_pd(rgbp[0], const2713), _mm_sub_pd(_mm_set1_pd(400.0),rgbp[0])),_mm_set1_pd(1.0/0.42));
rgbp[0] = _mm_mul_pd(rgbp[0],G3_SSE); //RGBp[0][0] ,RGBp[0][1] are ready
rgbp[0] =_mm_xor_pd(temp1,rgbp[0]); //RGBp[0,1] = RGBpa[0][0,1] < 0.1 ? - RGBp[0][0,1] : RGBp[0][0,1]; 

2. Bilateral Filter

We applied most of the optimization steps described above for the XYZ to CIE-CAM to the Bilateral Filter as well. Now, we will focus on some unique features of the Bilateral Filter optimization process.

For each pixel in Bilateral Filter single UNSIGNED CHAR color intensity component was used.  But inside the filter calculations we have SIGNED INT data. It means that the data quadruple could be processed by the SSE register and therefore we need to group data by four to make them SSE friendly.

So just moving from this code:

 for( j = matchWindow; j <= image->width -(matchWindow) ; j ++){
	*outData = Bfilter(data,image,i,j); //for every pixel do bilateral filter computation
	data++; 	outData++;

To this code:

 for( j = matchWindow; j <= image->width -(matchWindow) - 4; j += 4){
	 Bfilter4(data, outData, image,i,j); //for every 4 pixels do bilateral filtercomputation
	data+=4;  	outData+=4;	
for(int jj= j; jj< image->width-(matchWindow); jj++){
	*outData = Bfilter(data,image,i,jj); //for every pixel do bilateral filter computation
	data++; 	outData++;

This resulted in approximately a 35% speed-up even before SSE optimization.


In this work, the main optimization and benchmark target was an Intel® Xeon® processor 5500-based system, a CPU with Intel® quad-core technology and two physical sockets. The platform can support eight concurrent physical threads. We found that enabling Simultaneous Multi Threading (SMT), the interleaving of two logical threads of execution on a single physical, was effective. Enabling SMT let the system execute 16 logical threads. In the case of the XYZ to CIE-CAM algorithm, utilizing these 16 virtual cores (16 threads) gave us a 50 percent performance boost. A second significant enhancement with the Intel® Xeon® processor 5500 architecture is its NUMA (Non-Uniform Memory Access). With NUMA, each CPU has a local memory bank and a remote memory bank. Access times to the local memory bank are shorter than for the remote memory bank. This architectural change improves the overall memory bandwidth. Nevertheless, since access latency to the remote memory is ~1.7 longer than to local memory, threads should strive to access local memory as much as possible. This requires setting thread affinity and a careful design of allocations. In the general case, the optimizing applications for NUMA can gain up to 20 percent performance boost; however, we have witnessed performance drops of up to 50 percent on non-optimized applications. For that reason, and as can be seen from the results, it was usually better to disable NUMA in the BIOS configurations as recommended for non-NUMA optimizations.

Local Memory Access

The following results show our results on three of the algorithms. The performance for the XYZ to CIE-CAM color conversion and Bilateral Filter algorithms was application run-time in seconds (smaller numbers are better). The Halftone algorithm performance was measured in MB/second (larger numbers are better). All measurements were done on a dual socket, Intel® Xeon® processor 5500-based system running at 3.2 GHz. Baseline results are reported for the original, serial, version of the code and were measured on a previous generation Intel® Xeon® 5400 processor-based system. This platform is a dual socket, quad core platform with clock speed of 2.83 GHz.

Table 1 summarizes the results.  Column 2 is the original, serial code, compiled with Visual Studio* 2005 (VS). In column 3 we incorporate software optimizations (i.e. vectorization and parallelization) and move to the Intel® C++ Compiler. 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. Note: for each column, we made several runs and averaged the results to ensure reproducibility of the results.

We observed that the bulk improvement in run time was from incorporating multi-threading (OpenMP) and vectorization (SSE) to the baseline code. 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 best performing setting (SMT on, NUMA off) gave us a 50 percent performance increase over the older Intel® Xeon® processor 5400 machine. The overall gains from both software optimization and from moving to the new Intel® Xeon® processor 5500 platform were up to 68X relative to the baseline. A more visual representation of the results can be seen in Figure 6.


Intel Xeon5400 + Visual Studio 2005 (serial version)

Intel Xeon5400 + 
IC + SW optimizations (OMP + SSE)

Intel Xeon5500 +
IC + SW optimizations (SMT* Off) (NUMA OFF)

Intel Xeon5500 (SMT* ON) (NUMA ON)

Intel Xeon5500 (SMT* ON) (NUMA OFF)

speed up








Bilateral Filter







Halftoning (screening)







SMT* – Simultaneous Multi Threading

Table 1 : This table summarizes the optimization stages that were implemented. 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.
IC: Intel® C++ Compiler for Windows 11.1


Figure 6 : Bar charts of the optimization stages. The left chart shows XYZ to CIE-CAM and Bilateral Filter benchmarks while the right chart is focused on Halftoning results.


The inherent parallel structure of many image processing algorithms makes them suitable for both thread level parallelism and low data level parallelism. For the three algorithms we optimized, we achieved an excellent performance boost, which implies that thread synchronization overhead remains minimal. 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. In addition, since most image processing algorithms apply the same operations to each pixel, utilizing SSE was relatively simple and highly effective. Finally, we showed that when the data layout is designed to be SIMD-friendly, vectorization from the compiler provide a significant performance boost.


Guy Ben Haim is a senior application engineer in Intel Corporation in the Software and Services Group (SSG). Guy is working on optimizing applications to take advantage of the latest Intel software and hardware innovations. Guy provides technical training, consultation, and hands-on assistance to SW developers in areas of software optimization and parallel programming. 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. Prior to Intel, Guy worked for several startup up companies in the fields of communication, computers HW and semiconductors. Guy holds a B.Sc. from Tel Aviv University and M.B.A from Technion - Israel Institute of Technology.

Sagi Schein, Ph.D. is research scientist at HP labs. Sagi's research fields are image processing, computer vision and computer graphics. In particular, Sagi is focusing on accelerating the performance of imaging algorithms on parallel computing platforms such as multi-core machines and GPUs. Sagi has been at HP Labs for 4 years, prior to that Sagi was a senior software engineer for Qualcomm. Sagi holds a PhD in computer science from the Technion - Israel Institute of Technology.

Victoria Zhislina is a Senior Application Engineer at Intel Corporation in the Consumer Software Enabling team. Victoria’s responsibilities include helping software vendors in optimizing and\or porting their applications for Intel’s latest desktop and mobile processors. Her main areas of expertise are: Multi-core (parallel) programming; Intel Integrated Graphics (GMA) and the upcoming Intel Larrabee GPU. Also Victoria is involved in Intel hardware and software technology evangelization to the European software development community. Victoria has been working in Intel for 10 years and holds a BS in Applied Physics from the University of Nizhny Novgorod, Russia.


  • [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. 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
  • [TBB] http://www.threadingbuildingblocks.org

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