Success Is Dependent On Math - 1220 Words

Although mathematics and film are not commonly associated together, most films’ success is dependent on math. Various formulas are required in many aspects of film, such as budgeting and creating a consistent pattern of camera shots. Film editing, specifically, very rigorously involves mathematics through the use of algorithms in filters, percentages in creating an effect sequence, adjusting the x-axis and y-axis when motion tracking an effect to an object in a shot to create a special effect. Filters and blurs incorporate algorithms to impose an adjustment upon an image. Films incorporate these blurs and filters to achieve a certain feel or mood to a shot sequence, or to focus the audience’s attention of a specific detail if the†¦show more content†¦The pixel produced will overwrite the output image, producing a filter or blur. Figure 2: Sample Calculation of a Basic Filter or Blur Image from www.youtube.com/computerphile If all the kernel values are equal to 1, the image will be very blurred since the kernel value is equal to 1. This value of â€Å"1† is equal to the effect having a 100% opacity over the image, producing a very blurry edit (see Figure 3) Figure 3: 100% Blurriness of a Kernel in Video Editing Image from Personal Editing Software, Adobe Premiere Pro The â€Å"Gaussian Blur† is the most popular blur filter used in everyday video editing. It blurs the image, but the edges are somewhat crisp. This effect is produced by a normal distribution bell curve. The standard deviation determines how wide the bell curve is. If the numbers produced from the values of the normal distribution curve are weighted, the gaussian blur is produced. To exemplify this process, a 3 by 3 grid of a kernel can be made with a number in the middle. The further away one gets from this middle value gives less weight in the average, producing a moderate blur with a preserved edge. The standard deviation or ‘radius’ options in video editors actually refers to the standard deviation that produces the kernel in the gaussian blur. A sample calculation would to take a numbered pixel from the original image and then multiply it by each value in the