Embedded Image Processing on the TMSCTM DSP: Examples in Code Composer StudioTM and MATLAB [Shehrzad Qureshi] on The TMSC Line of DSPs (Image Processing). Even though this topic has a narrow focus, it calls for a wide array of tools, some hardware (DSP. Written for practicing signal and image processing engineers, this book discusses the implementation of advanced image processing algorithms on.

Author: | Balkis Meztisar |

Country: | Bahamas |

Language: | English (Spanish) |

Genre: | Technology |

Published (Last): | 8 April 2009 |

Pages: | 279 |

PDF File Size: | 4.62 Mb |

ePub File Size: | 8.98 Mb |

ISBN: | 278-6-26236-748-7 |

Downloads: | 15622 |

Price: | Free* [*Free Regsitration Required] |

Uploader: | Vogis |

The procesing argument to this function is the input image, and if a second argument, a histogram vector, is provided h i s t e q performs histogram specification. Omissions from this list are inadvertent. If pf r and pg s denote the PDFs of the input and output images, respectively, then the total number of input pixels with gray- level values lying between r and Ar is pf r Ar.

## Embedded Image Processing on the TMS320C6000 DSP, w. CD-ROM

With fixed-point arithmetic, the proper scaling of the data is of the utmost importance. The first and second arguments are the image matrix and filter kernel. Ease of development is proportional to the quality of the tools at your disposal, and in this chapter all of the tools encountered in embecded book are formally introduced.

In short, image embeddwd is both very compute- and data-intensive. In this case, the compiler will be forced to pad the fetch packet with NOP no operation instructions. Reduction of quantum mottle noise by image smoothing image courtesy of Neil Trent, Dept. All of these programs are fixed-point implementations, using a Q15 data format. The unsharp mask is demonstrated in Figure When interpreting a Q0.

### Embedded Image Processing on the TMSC DSP, w. CD-ROM Buch

Hence, the compiler, rather than the hardware, is the driving element behind taking advantage of any ILP. There remain a few caveats: Conversion of a floating-point number into Q15 format is accomplished by first multiplying by – which is equivalent to shifting left by 15 bits in an integer representation – and then truncating down to an integer, by lopping off whatever fractional component remains after the multiplication.

Achieving these goals takes time, and as such we are somewhat constrained by space. Eventually, the cost of transforming two matrices to the frequency domain via the 2D FFT and one matrix back to the time domain via the 2D inverse FFT is balanced out by the fact that in between these two transforms, element-wise multiplication occurs, as opposed to sliding a mask across an image and computing the sum of products at each step.

The C6x architecture is unique in that it is bi-endian, meaning that depending on the build options it can be either little or big endian. Zvr added it May 17, In the former case, a classic example in astronomical imaging was the initial Hubble telescope, where imperfections in the main mirror caused sub-standard image acquisition. Inspection of the histogram is a reasonable first indicator of the overall appearance of an image, as demonstrated by Figurewhich shows a dark and bright image along with their associated histograms.

This temporary image result could then be transposed the rows become the columns and vice-versaand the same process run again, except this time as the algorithm marches down the row dimension, the columns are now transformed.

A full description of the entire C6x architecture is covered in [], so in this tmw320c6000 we instead focus on explaining embedder rationale and motivation behind VLIW and how it fits into the C architecture, before moving to an important discussion on the difference between fixed-point and floating-point architectures.

If QMULT is to be used as part of another computation further down the road, we will need to realign this number by bit shifting to isolate the fractional bits we wish to retain, taking extreme care to guard against overflow.

Image noise may be the result of a variety of sources, including: Initially they were rhe used in ID signal processing applications like various telecommunication and audio applications, and today this largely remains the case.

Suppose as part of an embedded application, you had to implement […]. This book features a variety of Tl-related software toolkits. That being said, there is one resource above all others that anyone serious about C6x development should be aware of, and that is the Yahoo!

How would you go about testing your C6x implementation? The conv2 function always assumes zero padding of edge pixels, and by passing in the string A v a hms320c6000 i d ‘conv2 returns just the portion of the convolution that did not require zero-padding. Thankfully, in the world of DSP this situation is rare.

The image visualization feature of CCStudio is quite full-featured, in fact if you click within the processnig window the pixel coordinates and tns320c6000 intensity is displayed in the status bar at the bottom of the display. Code Composer Studio provides numerous data visualization tools.

So in lieu of a for loop that walks through each pixel in I and replaces that pixel with the value given by the LUT, a single indexing statement suffices.

### Embedded Image Processing on the TMSC DSP

I have also been blessed to have had the pleasure of working with many brilliant engineers and scientists at various employment stops along the way, and without them to learn from I could never have developed my love of algorithms and programming.

However, since processung exact location of any faulty pixel is presumably known a priori, it follows that a far more efficient and effective teh of reducing this highly structured noise is to generate a list of image locations that should be filtered, and apply tye averaging mask over just those locations. After the theoretical groundwork has been laid, examples in MATLAB are used to drive home the core concepts behind the algorithm in question, without encumbering the reader with the details that inevitably will follow.

GPPs are designed to perform a diverse range of computing tasks many of them not numerically oriented and typically run heavy-weight operating systems definitely not ekbedded for embedded and especially real-time systems. One of the reasons why application development on the PC can sometimes appear daunting is the overwhelming array of class libraries, frameworks, and Application Programming Interfaces APIs made available to users to Embfdded Studio.

As one might expect, there are other types of linear filters, for example high-pass filters which have the opposite effect of low- pass filters. Contrast stretching is a common technique, and can be quite effective if utilized properly. MFC dialog encapsulated in coialog-derived class Figure Such a situation is analogous to using too high of a flash level when taking photographic images, thereby producing a “washed out” image. The dynamic range of an image is defined to be the entire range of intensity values contained within an image, or put a simpler way, the maximum pixel value minus the minimum pixel value.

The output of a filter obtained by feeding it an impulse is called the filter’s impulse response, and the impulse response completely characterizes the filter.

## Embedded Image Processing on the Tms320c6000 DSP: Examples in Code Composer Studio and MATLAB

The integration of these disparate components and frameworks is managed by the I m a g e 8 b p p class, introduced in Chapter 3. Remember, a floating-point processor is a superset of a fixed-point processor, and there is not much required to get a working fixed-point algorithm running efficiently on a floating-point architecture. Algorithm delineated an efficient means of performing histogram specification that does not explicitly equalize the input image.

While it is often tempting to focus on low-level optimizations e. Then, if the performance of the algorithm is still unsatisfactory, lower-level optimizations begin to take on added importance. An image Figure This inverse transform will in turn satisfy both 1 and 2.

The program now reports approximately 34, cycles versus 35, for the floating-point loop, and approximately cycles versus for the fixed-point loop.

First and foremost, my soon-to- be wife, Lubna, provided invaluable feedback with regards to the imate of the book and spent countless hours reviewing drafts of the material. Both libraries are highly optimized implementations, and come standard with CCStudio. For our purposes, it is sufficient to say that 2D convolution can be performed in the frequency domain by exploiting one of the central tenets of signal processing theory, the convolution theorem, which states that convolution in the time domain is equivalent to multiplication in the frequency domain.