4K Display Technology

Presented by:Kashish soni(13ITU013)Abhishek Garg (13ITU002)

Presentation Outline

Introduction Motivation 1080P V/S 4K resolution 4K Resolution Specifications Image sampling Image quantization Dithering Conclusion

Introduction

4K is the new big thing in display technology. 4K denotes a very specific display resolution of 4096 x 2160

megapixels and aspect ratio (16:9) 4K is also known as Ultra High Definition(UHD). 4k TVs have increased their market share in 2014. Every brand has

a few of them now, but the prices are still above mainstream level. It will soon become a format for both broadcast TV and Blue ray.

Motivation

The high definition comes in two flavors which are 720p (HD ready) and 1080p (Full HD).

Both standards offer more picture information than the standard definition formats.

Ultra HD takes that on to the next level. A high pixel count also enables images to go larger before they

break up, which suits the trend to bigger TVs. The real difference between 720p and 1080p is the minimum

viewing distance from TV about two meters.

1080p vs. 4K resolution

A High Definition TV with 1080p resolution is composed of two million pixels (1920 x 1080), while a 4K TV (aka Ultra High Definition) has over eight million pixels (4096 x 2160). Therefore, 4K has around four times more resolution than 1080p and produces a clearer picture.

Increasing pixel density leads to closer viewing distance without the pixel grid becoming obvious to viewer.

A 4K image - or Ultra HD - enables user to sit 1.6m from the screen.

4K Resolution (Ultra HD)

4K Resolution Specifications

"Ultra High-Definition", or "Ultra HD",

Have an aspect ratio of at least 16:9 Can present native video at a minimum

resolution of 3,840 x 2,160 pixels. 4K UHDTV (2160p) is 3840 pixels wide by 2160 pixels tall (8.3

megapixels), which is four times more pixels than 1920 x 1080 (2.1 megapixels).

8K UHDTV (4320p) is 7680 pixels wide by 4320 pixels tall (33.2 megapixels), which is sixteen times more pixels than current 1080p HDTV, which brings it closer to the detail level of 15/70 mm IMAX.

The p in 2160p and 4320p stands for progressive scan or non-interlaced.

In order to become suitable for computer processing, an image function f(x,y) must be digitized both spatially and in amplitude. Typically, a camera is used to digitize an image. A frame grabber or digitizer is used  to sample and quantize the analog video signal and store it in the so called frame buffer. The sampling rate (or pixel clock) determines the spatial resolution of the digitized image, while the quantization level determines the number of grey levels in the digitized image.  

IMAGE SAMPLING

A continuous image f(x,y) is normally approximated by equally spaced samples arranged in the form of an N x M array where each element of the array is a discrete quantity.

The sampling rate (or pixel clock)  of the digitiser determines the spatial resolution of the digitized image.

The finer the sampling (i.e., the larger M and N)  the better the approximation of the continuous image function f(x,y).

IMAGE QUANTIZATION

A magnitude of the sampled image is expressed as a digital value in image processing. The transition between continuous values of the image function (brightness) and its digital equivalent is called quantitation. The number of quantization levels should be high enough for human perception of fine shading details in the image. The occurrence of false contours is the main problem in image which have been quantized with insufficient brightness levels. This effect arises when the number of brightness levels is lower than that which humans can easily distinguish. This number is dependent on many factors -- for example, the average local brightness -- but displays which avoids this effect will normally provide a range of at least 100 intensity levels.

Most digital image processing devices use quantization into k equal intervals. If b bits are used ... the number of brightness levels is k=2powb.

Eight bits per pixel are commonly used, specialized measuring devices use 12 and more bits per pixel.

A magnitude of the sampled image is expressed as a digital value in image processing.The transition between continuous values of the image function (brightness) and its digital equivalent is called quantitation. The number of quantitation levels should be high enough for human perception of fine shading details in the image. The occurrence of false contours is the main problem in image which have been quantized with insufficient brightness levels. This effect arises when the number of brightness levels is lower than that which humans can easily distinguish. This number is dependent on many factors -- for example, the average local brightness -- but displays which avoids this effect will normally provide a range of at least 100 intensity levels.Most digital image processing devices use quantitation into k equal intervals.If   bits are used ... the number of brightness levels is  .

Eight bits per pixel are commonly used, specialized measuring devices use 12 and more bits per pixel.

DITHERING

Dithering is the process by which we create illusions of the color that are not present actually. It is done by the random arrangement of pixels.

Why dithering??

DITHERING WITH QUANTIZATION

When we perform quantization , to the last level , we see that the image that comes in the last level (level 2) looks like this.

Now as we can see from the image here , that the picture is not very clear, especially if you will look at the left arm and back of the image of the Einstein. Also this picture does not have much information or detail of the Einstein.

Now if we were to change this image into some image that gives more detail then this, we have to perform dithering.

Performing dithering. First of all , we will work on thresholding. Dithering is usually

working to improve thresholding. During thresholding, the sharp edges appear where gradients are smooth in an image.

In thresholding , we simply choose a constant value. All the pixels above that value are considered as 1 and all the value below it are considered as 0.

We got this image after thresholding. Since there is not much change in the image , as the values

are already 0 and 1 or black and white in this image.

now we perform some random dithering to it. Its some random arrangement of pixels.

We got an image that gives slighter of the more details , but its contrast is very low.

So we do some more dithering that will increase the contrast. The image that we got is this:

Now we mix the concepts of random dithering , along with threshold and we got an image like this.

Now you see , we got all these images by just re-arranging the pixels of an image. This re-arranging could be random or could be according to some measure.

Conclusion

Complex implementation. Requires a large shift in the broadcast and entertainment

infrastructure. Currently the only devices to offer Ultra HD play-out are ultra high

resolution PC graphics cards, which typically use a quartet of SDI or HDMI outputs to deliver 8MP of video.

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