advanced image scalling processor

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The IP00C705 is an advanced image processor that performs scaling and de-interlacing in

full 12-bit internal front-to-back processing resolution. It also features extended line

buffers to handle more than 1920 pixels per line, and allow for an easy integration ofmultiple devices working together. The IP00C705 also has a wide scaling core filter of

12x12 pixels that brings you broadcast-quality format conversion. The IP00c705 is

ideally suited for broadcast conversion products and high-end display systems.

Features

1. De-interlacing

o Temporal motion detection

o Enhanced diagonal interpolation

o All cadences supported

2. Noise filtering

o Block noise, Mosquito, temporal noise filtering

o Chroma error filter

3. Image scalingo 6x6 filter for image zoom

o 12x12 filter for image shrink

o Independent H and V scaling ratios

o Non-linear H and V scaling (aspect ratio correction)

o Dynamic scaling Frame Rate Conversion

o Independent clock, H and V sync. for the input and output ports

o Input/output frame synchronization

4. On-Screen Display

o Bitmap OSD featuring 256 colors, 64x64 pixels fonts

5. Other Features

o Support for xvYCC processingo Horizontal and vertical edge enhancement circuits

o Brightness and contrast adjustments

o 14-bit color Gamma correction tables (7 LUTs available)

o Dithering for 12, 10 or 8-bit output

o Color management

o Image flip (vertical, horizontal)

o 90 deg. Rotation

o Edge blending

o Vertical Keystone

6. External Memory

o DDR-SDRAM PC400 ( 256Mbit x32) x 2 or(256 /128 Mbit, x16) x 4

7. CPU Interface

o 8-bit parallel

o Flash memory interface: SPI, 100 MHz, up to 128 Mbit

8. Power Supply

o 3.3V /2.5V/1.2V

9. Package


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o 508-pin plastic BGA (Body size 27mm, Ball pitch 1.0mm)

Input/Output

1. Input

o 36-bit RGB / 36-bit YUV444 / 24-bit YUV422 at 166 MHz or 83 MHz inDDR mode

o 12-bit YUV422 (Bt656) at 166 MHz

o Progressive or interlaced formats

o Up to 4096 pixels per line, with 2176 active pixels

o External synchronization

2. Output

o 36-bit RGB / 36-bit YUV444 / 24-bit YUV422 at 166 MHz or 83 MHz in

DDR mode

o 12-bit YUV422 (Bt656) at 166 MHz

o Progressive or interlaced formats

o Up to 4096 pixels per line, with 2176 active pixelso Internal/External synchronization

Image scaling

From Wikipedia, the free encyclopedia

Jump to: navigation,search

An image scaled with nearest-neighbor scaling (left) and 2SaI scaling (right).

In computer graphics, image scaling is the process of resizing a digital image. Scaling isa non-trivial process that involves a trade-off between efficiency, smoothness and

sharpness. Withbitmap graphics, as the size of an image is reduced or enlarged, the

pixels which comprise the image become increasingly visible, making the image appear"soft" if pixels are averaged, or jagged if not. With vector graphics the trade-off may be

in processing power for re-rendering the image, which may be noticeable as slow re-rendering with still graphics, or slowerframe rate and frame skipping in computeranimation.

Apart from fitting a smaller display area, image size is most commonly decreased (or

subsampled or downsampled) in order to produce thumbnails. Enlarging an image

(upsampling orinterpolating) is generally common for making smaller imagery fit abigger screen in fullscreen mode, for example. In zooming a bitmap image, it is not
http://en.wikipedia.org/wiki/Image_scaling#mw-navigationhttp://en.wikipedia.org/wiki/Image_scaling#mw-navigationhttp://en.wikipedia.org/wiki/Image_scaling#p-searchhttp://en.wikipedia.org/wiki/Digital_imagehttp://en.wikipedia.org/wiki/Bitmaphttp://en.wikipedia.org/wiki/Pixelhttp://en.wikipedia.org/wiki/Vector_graphicshttp://en.wikipedia.org/wiki/Frame_ratehttp://en.wikipedia.org/wiki/Computer_animationhttp://en.wikipedia.org/wiki/Computer_animationhttp://en.wikipedia.org/wiki/Thumbnailshttp://en.wikipedia.org/wiki/Thumbnailshttp://en.wikipedia.org/wiki/Interpolationhttp://en.wikipedia.org/wiki/File:2xsai_example.pnghttp://en.wikipedia.org/wiki/Image_scaling#mw-navigationhttp://en.wikipedia.org/wiki/Image_scaling#p-searchhttp://en.wikipedia.org/wiki/Digital_imagehttp://en.wikipedia.org/wiki/Bitmaphttp://en.wikipedia.org/wiki/Pixelhttp://en.wikipedia.org/wiki/Vector_graphicshttp://en.wikipedia.org/wiki/Frame_ratehttp://en.wikipedia.org/wiki/Computer_animationhttp://en.wikipedia.org/wiki/Computer_animationhttp://en.wikipedia.org/wiki/Thumbnailshttp://en.wikipedia.org/wiki/Interpolation


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possible to discover any more information in the image than already exists, and image

quality inevitably suffers. However, there are several methods of increasing the number

of pixels that an image contains, which evens out the appearance of the original pixels.

Scaling methods

An image size can be changed in several ways. Consider doubling the size of the

following image:

Nearest-neighbor interpolation

One of the simpler ways of doubling its size is nearest-neighbor interpolation, replacingevery pixel with four pixels of the same color:

The resulting image is larger than the original, and preserves all the original detail, buthas undesirablejaggedness. The diagonal lines of the W, for example, now show the

characteristic "stairway" shape.

Other scaling methods below are better at preserving smooth contours in the image:

Bilinear interpolation

For example,bilinear interpolationproduces the following result:

Linear (or bilinear, in two dimensions) interpolation is typically good for changing the

size of an image, but causes some undesirable softening of details and can still be
http://en.wikipedia.org/wiki/Nearest-neighbor_interpolationhttp://en.wikipedia.org/wiki/Jaggieshttp://en.wikipedia.org/wiki/Bilinear_interpolationhttp://en.wikipedia.org/wiki/Bilinear_interpolationhttp://en.wikipedia.org/wiki/File:Image-after-linear-interpolation.pnghttp://en.wikipedia.org/wiki/File:Image-after-trivial-scaling.pnghttp://en.wikipedia.org/wiki/File:Image-before-scaling.pnghttp://en.wikipedia.org/wiki/Nearest-neighbor_interpolationhttp://en.wikipedia.org/wiki/Jaggieshttp://en.wikipedia.org/wiki/Bilinear_interpolation


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somewhat jagged. Better scaling methods includebicubic interpolation(example below)

and Lanczos resampling.

hqx

For magnifying computer graphics with low resolution and/or few colors (usually from 2

to 256 colors), better results will be achieved by hqx or otherpixel art scaling algorithms.

These produce sharp edges and maintain high level of detail.

Supersampling

For scaling photos (and raster images with many colors), see also anti-aliasing algorithmscalledsupersampling.

Vectorization

An entirely different approach is vector extraction or vectorization. Vectorization first

creates a resolution independent vector representation of the graphic to be scaled. Thenthe resolution-independent version is rendered as a raster image at the desired resolution.

This technique is used by Adobe Live Trace, inkscape, and several recent papers.[1]
http://en.wikipedia.org/wiki/Bicubic_interpolationhttp://en.wikipedia.org/wiki/Lanczos_resamplinghttp://en.wikipedia.org/wiki/Hqxhttp://en.wikipedia.org/wiki/Pixel_art_scaling_algorithmshttp://en.wikipedia.org/wiki/Pixel_art_scaling_algorithmshttp://en.wikipedia.org/wiki/Raster_imageshttp://en.wikipedia.org/wiki/Supersamplinghttp://en.wikipedia.org/wiki/Supersamplinghttp://en.wikipedia.org/wiki/Image_scaling#cite_note-1http://en.wikipedia.org/wiki/File:Image_scaling_example_image_after_applying_inkscape_vectorization.pnghttp://en.wikipedia.org/wiki/File:Image-after-hq2x.pnghttp://en.wikipedia.org/wiki/File:Image-after-cubic-interpolation.pnghttp://en.wikipedia.org/wiki/Bicubic_interpolationhttp://en.wikipedia.org/wiki/Lanczos_resamplinghttp://en.wikipedia.org/wiki/Hqxhttp://en.wikipedia.org/wiki/Pixel_art_scaling_algorithmshttp://en.wikipedia.org/wiki/Raster_imageshttp://en.wikipedia.org/wiki/Supersamplinghttp://en.wikipedia.org/wiki/Image_scaling#cite_note-1


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Algorithms

Two standard scaling algorithms arebilinearandbicubic interpolation. Filters like these

work by interpolating pixel color values, introducing a continuous transition into theoutput even where the original material has discrete transitions. Although this is desirable

for continuous-tone images, some algorithms reduce contrast (sharp edges) in a way thatmay be undesirable for line art.

Nearest-neighbor interpolation preserves these sharp edges, but it increases aliasing(orjaggies; where diagonal lines and curves appear pixelated). Several approaches have been

developed that attempt to optimize for bitmap art by interpolating areas of continuous

tone, preserve the sharpness of horizontal and vertical lines and smooth all other curves.

Pixel art scaling algorithms

Aspixel art graphics are usually in very low resolutions, they rely on careful placing of

individual pixels, often with a limited palette of colors. This results in graphics that relyon a high amount stylized visual cues to define complex shapes with very little

resolution, down to individual pixels.

As such, a number of specialized algorithms have been developed to handle pixel art

graphics, as the traditional scaling algorithms do not take such perceptual cues into

account.

Efficiency

Since a typical application of this technology is improving the appearance offourth-

generation and earliervideo games on arcadeandconsole emulators, many are designedto run in real time for sufficiently small input images at 60 frames per second.

Many work only on specific scale factors: 2 is the most common, with 3 and 4 alsopresent.

EPX/Scale2/AdvMAME2

Eric's Pixel Expansion (EPX) is an algorithm developed by Eric Johnston at LucasArts

around 1992,[2] when porting the SCUMM engine games from the IBM PC (which ran at320200256 colors) to the early color Macintosh computers, which ran at more or less

double that resolution.[3] The algorithm works as follows:
http://en.wikipedia.org/wiki/Bilinear_filteringhttp://en.wikipedia.org/wiki/Bilinear_filteringhttp://en.wikipedia.org/wiki/Bilinear_filteringhttp://en.wikipedia.org/wiki/Bicubic_interpolationhttp://en.wikipedia.org/wiki/Bicubic_interpolationhttp://en.wikipedia.org/wiki/Bicubic_interpolationhttp://en.wikipedia.org/wiki/Display_contrasthttp://en.wikipedia.org/wiki/Nearest-neighbor_interpolationhttp://en.wikipedia.org/wiki/Aliasinghttp://en.wikipedia.org/wiki/Aliasinghttp://en.wikipedia.org/wiki/Jaggieshttp://en.wikipedia.org/wiki/Pixel_arthttp://en.wikipedia.org/wiki/History_of_video_game_consoles_(fourth_generation)http://en.wikipedia.org/wiki/History_of_video_game_consoles_(fourth_generation)http://en.wikipedia.org/wiki/Video_gamehttp://en.wikipedia.org/wiki/Video_gamehttp://en.wikipedia.org/wiki/Arcade_emulatorhttp://en.wikipedia.org/wiki/Arcade_emulatorhttp://en.wikipedia.org/wiki/Console_emulatorhttp://en.wikipedia.org/wiki/Console_emulatorhttp://en.wikipedia.org/wiki/LucasArtshttp://en.wikipedia.org/wiki/Image_scaling#cite_note-epx_screenshot-2http://en.wikipedia.org/wiki/SCUMMhttp://en.wikipedia.org/wiki/Image_scaling#cite_note-mactech-3http://en.wikipedia.org/wiki/Bilinear_filteringhttp://en.wikipedia.org/wiki/Bicubic_interpolationhttp://en.wikipedia.org/wiki/Display_contrasthttp://en.wikipedia.org/wiki/Nearest-neighbor_interpolationhttp://en.wikipedia.org/wiki/Aliasinghttp://en.wikipedia.org/wiki/Jaggieshttp://en.wikipedia.org/wiki/Pixel_arthttp://en.wikipedia.org/wiki/History_of_video_game_consoles_(fourth_generation)http://en.wikipedia.org/wiki/History_of_video_game_consoles_(fourth_generation)http://en.wikipedia.org/wiki/Video_gamehttp://en.wikipedia.org/wiki/Arcade_emulatorhttp://en.wikipedia.org/wiki/Console_emulatorhttp://en.wikipedia.org/wiki/LucasArtshttp://en.wikipedia.org/wiki/Image_scaling#cite_note-epx_screenshot-2http://en.wikipedia.org/wiki/SCUMMhttp://en.wikipedia.org/wiki/Image_scaling#cite_note-mactech-3


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Image Size

You should keep in mind that an image can be located in one of four places: in the

image file, in RAM after it has been loaded, on your screen when it is displayed,or on paper after it has been printed. Scaling the image changes the number of

pixels (the amount of information) the image contains, so it directly affects the

amount of memory the image needs (in RAM or in a file).

However printing size also depends upon the resolution of the image, whichessentially determines how many pixels there will be on each inch of paper. If you

want to change the printing size without scaling the image and changing the

number of pixels in it, you should use the Print Size dialog. The screen size

depends not only on the number of pixels, but also on the screen resolution, thezoom factor and the setting of the Dot for Dotoption.

If you enlarge an image beyond its original size, GIMP calculates the missing

pixels by interpolation, but it does not add any new detail. The more you enlargean image, the more blurred it becomes. The appearance of an enlarged image

depends upon the interpolation method you choose. You may improve the

appearance by using theSharpen filter after you have scaled an image, but it is
http://docs.gimp.org/en/gimp-image-print-size.htmlhttp://docs.gimp.org/en/gimp-view-dot-for-dot.htmlhttp://docs.gimp.org/en/gimp-view-dot-for-dot.htmlhttp://docs.gimp.org/en/plug-in-sharpen.htmlhttp://docs.gimp.org/en/plug-in-sharpen.htmlhttp://docs.gimp.org/en/gimp-image-print-size.htmlhttp://docs.gimp.org/en/gimp-view-dot-for-dot.htmlhttp://docs.gimp.org/en/plug-in-sharpen.html


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best to use high resolution when you scan, take digital photographs or produce

digital images by other means. Raster images inherently do not scale up well.

You may need to reduce your image if you intend to use it on a web page. Youhave to consider that most internet users have relatively small screens which

cannot completely display a large image. Many screens have a resolution of1024x768 or even less.

Adding or removing pixels is called Resampling.

Width; Height

When you click on the Scale command, the dialog displays the dimensions of the

original image in pixels. You can set the Width and the Height you want to give

to your image by adding or removing pixels. If the chain icon next to the Width

and Height boxes is unbroken, the Width and Height will stay in the same

proportion to each other. If you break the chain by clicking on it, you can set themindependently, but this will distort the image.

However, you do not have to set the dimensions in pixels. You can choosedifferent units from the drop-down menu. If you choose percent as the units, you

can set the image size relative to its original size. You can also use physical units,

such as inches or millimeters. If you do that, you should set the X resolution and

Y resolution fields to appropriate values, because they are used to convertbetween physical units and image dimensions in pixels.

X resolution; Y resolution

You can set the printing resolution for the image in the X resolution and Y

resolution fields. You can also change the units of measurement by using thedrop-down menu.

Quality

To change the image size, either some pixels have to be removed or new pixels

must be added. The process you use determines the quality of the result. The

Interpolation drop down list provides a selection of available methods ofinterpolating the color of pixels in a scaled image:

Interpolation

None: No interpolation is used. Pixels are simply enlarged or removed, as

they are when zooming. This method is low quality, but very fast.

Linear: This method is relatively fast, but still provides fairly good results.


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Cubic: The method that produces the best results, but also the slowest

method.

Sinc (Lanczos 3): New with GIMP-2.4, this method gives less blur in

important resizings.

DIGITAL IMAGE INTERPOLATION

Image interpolation occurs in all digital photos at some stage whether this be inbayer

demosaicing or in photo enlargement. It happens anytime you resize or remap (distort)your image from one pixel grid to another. Image resizing is necessary when you need to

increase or decrease the total number of pixels, whereas remapping can occur under a

wider variety of scenarios: correcting for lens distortion, changing perspective, and

rotating an image.

Even if the same image resize or remap is performed, the results can vary significantly

depending on the interpolation algorithm. Itis only an approximation, therefore an image

will always lose some quality each time interpolation is performed. This tutorial aims to

provide a better understanding of how the results may vary helping you to minimizeany interpolation-induced losses in image quality.

CONCEPT

Interpolation works by using known data to estimate values at unknown points. For

example: if you wanted to know the temperature at noon, but only measured it at 11AMand 1PM, you could estimate its value by performing a linear interpolation:

If you had an additional measurement at 11:30AM, you could see that the bulk of thetemperature rise occurred before noon, and could use this additional data point to perform

a quadratic interpolation:
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The more temperature measurements you have which are close to noon, the more

sophisticated (and hopefully more accurate) your interpolation algorithm can be.

IMAGE RESIZE EXAMPLE

Image interpolation works in two directions, and tries to achieve a best approximation of

a pixel's color and intensity based on the values at surrounding pixels. The following

example illustrates how resizing / enlargement works:

2D Interpolation

183%

Original Before After No Interpolation

Unlike air temperature fluctuations and the ideal gradient above, pixel values can changefar more abruptly from one location to the next. As with the temperature example, the

more you know about the surrounding pixels, the better the interpolation will become.

Therefore results quickly deteriorate the more you stretch an image, and interpolation can

never add detail to your image which is not already present.

IMAGE ROTATION EXAMPLE

Interpolation also occurs each time you rotate or distort an image. The previous example

was misleading because it is one which interpolators are particularly good at. This next

example shows how image detail can be lost quite rapidly:

Image Degrades

Rotation

Original 45 Rotation 90 Rotation 2 X 45 6 X 15


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(Lossless) Rotations Rotations

The 90 rotation is lossless because no pixel ever has to be repositioned onto the border

between two pixels (and therefore divided). Note how most of the detail is lost in just thefirst rotation, although the image continues to deteriorate with successive rotations. One

should therefore avoid rotating your photos when possible; if an unleveled photorequires it, rotate no more than once.

The above results use what is called a "bicubic" algorithm, and show significantdeterioration. Note the overall decrease in contrast evident by color becoming less

intense, and how dark haloes are created around the light blue. The above results could be

improved significantly, depending on the interpolation algorithm and subject matter.

TYPES OF INTERPOLATION ALGORITHMS

Common interpolation algorithms can be grouped into two categories: adaptive and non-

adaptive. Adaptive methods change depending on what they are interpolating (sharpedges vs. smooth texture), whereas non-adaptive methods treat all pixels equally.

Non-adaptive algorithms include: nearest neighbor, bilinear, bicubic, spline, sinc,lanczos and others. Depending on their complexity, these use anywhere from 0 to 256 (or

more) adjacent pixels when interpolating. The more adjacent pixels they include, the

more accurate they can become, but this comes at the expense of much longer processing

time. These algorithms can be used to both distort and resize a photo.

Original

250%

Adaptive algorithms include many proprietary algorithms in licensed software such as:Qimage, PhotoZoom Pro, Genuine Fractals and others. Many of these apply a different

version of their algorithm (on a pixel-by-pixel basis) when they detect the presence of anedge aiming to minimize unsightly interpolation artifacts in regions where they aremost apparent. These algorithms are primarily designed to maximize artifact-free detail in

enlarged photos, so some cannot be used to distort or rotate an image.


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NEAREST NEIGHBOR INTERPOLATION

Nearest neighbor is the most basic and requires the least processing time of all the

interpolation algorithms because it only considers one pixel the closest one to theinterpolated point. This has the effect of simply making each pixel bigger.

BILINEAR INTERPOLATION

Bilinear interpolation considers the closest 2x2 neighborhood of known pixel values

surrounding the unknown pixel. It then takes a weighted average of these 4 pixels toarrive at its final interpolated value. This results in much smoother looking images than

nearest neighbor.

The diagram to the left is for a case when all known pixel distances are equal, so theinterpolated value is simply their sum divided by four.

BICUBIC INTERPOLATION

Bicubic goes one step beyond bilinear by considering the closest 4x4 neighborhood of

known pixels for a total of 16 pixels. Since these are at various distances from the

unknown pixel, closer pixels are given a higher weighting in the calculation. Bicubicproduces noticeably sharper images than the previous two methods, and is perhaps the

ideal combination of processing time and output quality. For this reason it is a standard in

many image editing programs (including Adobe Photoshop), printer drivers and in-camera interpolation.


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HIGHER ORDER INTERPOLATION: SPLINE & SINC

There are many other interpolators which take more surrounding pixels into

consideration, and are thus also much more computationally intensive. These algorithmsinclude spline and sinc, and retain the most image information after an interpolation.

They are therefore extremely useful when the image requires multiple rotations /distortions in separate steps. However, for single-step enlargements or rotations, thesehigher-order algorithms provide diminishing visual improvement as processing time is

increased.

INTERPOLATION ARTIFACTS TO WATCH OUT FOR

All non-adaptive interpolators attempt to find an optimal balance between three

undesirable artifacts: edge halos, blurring and aliasing.

Original

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Aliasing Blurring Edge Halo

Even the most advanced non-adaptive interpolators always have to increase or decreaseone of the above artifacts at the expense of the other two therefore at least one will be

visible. Also note how the edge halo is similar to the artifact produced by over sharpening

with an unsharp mask, and improves the appearance of sharpness by increasing acutance.

Adaptive interpolators may or may not produce the above artifacts, however they can alsoinduce non-image textures or strange pixels at small-scales:

Crop Enlarged220%

On the other hand, some of these "artifacts" from adaptive interpolators may also be seen

as benefits. Since the eye expects to see detail down to the smallest scales in fine-textured
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areas such as foliage, these patterns have been argued to trick the eye from a distance (for

some subject matter).

ANTI-ALIASING

Anti-aliasing is a process which attempts to minimize the appearance of aliased or jaggeddiagonal edges, termed "jaggies." These give text or images a rough digital appearance:

300%

Anti-aliasing removes these jaggies and gives the appearance of smoother edges andhigher resolution. It works by taking into account how much an ideal edge overlaps

adjacent pixels. The aliased edge simply rounds up or down with no intermediate value,

whereas the anti-aliased edge gives a value proportional to how much of the edge waswithin each pixel:

Ideal EdgeResampled to Low

Resolution

Ideal Edge on Low Resolution Grid Choose: Aliased Anti-Aliased

A major obstacle when enlarging an image is preventing the interpolator from inducing

or exacerbating aliasing. Many adaptive interpolators detect the presence of edges andadjust to minimize aliasing while still retaining edge sharpness. Since an anti-aliased

edge contains information about that edge's location at higher resolutions, it is also

conceivable that a powerful adaptive (edge-detecting) interpolator could at least partiallyreconstruct this edge when enlarging.

NOTE ON OPTICAL vs. DIGITAL ZOOMMany compact digital cameras can perform both an optical and a digital zoom. A camera

performs an optical zoom by moving the zoom lens so that it increases the magnificationof light before it even reaches the digital sensor. In contrast, a digital zoom degrades

quality by simply interpolating the image after it has been acquired at the sensor.
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10X Optical Zoom

10X Digital Zoom

Even though the photo with digital zoom contains the same number of pixels, the detail is

clearly far less than with optical zoom. Digital zoom should be almost entirely avoided,

unless it helps to visualize a distant object on your camera's LCD preview screen.Alternatively, if you regularly shoot in JPEG and plan on cropping and enlarging the

photo afterwards, digital zoom at least has the benefit of performing the interpolation

before any compression artifacts set in. If you find you are needing digital zoom toofrequently, purchase a teleconverter add-on, or better yet: a lens with a longer focal

length.

advanced image scalling processor

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