Anti Aliasing in Multimedia
It is a typical image quality problem on all pixel devices including computer screens. Aliasing is the stair-step effect or jaggies on the edges of objects displayed on the Computer Screen. All diagonal and curved lines display as a series of little zigzag horizontal and vertical lines. So, It can also distract PC users.
These jaggies essentially caused by the problem of trying to map a continuous image onto a discrete grid of pixels. This continuous-discrete-transformation says Scan Conversion. It performs by generating image pixels at an integer location that only approximates the true location of the sampled points. Pixels are generating at alias locations constitute aliases of the edges of the true object. Therefore we can say aliasing occurs as a result of an insufficient sampling rate and approximation error says Quantization Error.
Anti-Aliasing is a potential problem whenever an analogue signal is a point sampled to convert it into a digital signal. It can also occur in audio sampling. Thus the screen treats as if it has a higher resolution than what is. A virtual image calculates at the higher spatial resolution and then maps (displays) to the actual frame resolution after combining the results of the subpixels. The intensity value of a pixel is the average of the intensity values of all the sampled sub-pixels within that pixel.
There are mainly 3 types of methods of Anti-Aliasing.
1. Increase the resolution display (High Resolution)
Increase the resolution display:
As the aliasing problem is due to low resolution, one easy solution is to increase the resolution, causing sample points to occur more frequently. Using high resolution, the jaggies become so small that jagged edges get blurred out and edges appear smooth.
Post-filtering or Supersampling:
In this method, more than one sample is sampled per pixel. How? Every pixel area on the display surface assumes to subdivide into a grid of smaller sub-pixels, it says Super-Samples.
Pre-filtering or Area-Sampling:
Pre-filtering methods treat a pixel as an area and compute pixel colour based on the overlap of the scene’s objects with a pixel’s area. These techniques compute the shades of grey based on how much of a pixel’s area covers by an object.