4. Optimization - radhikahorti/Dynamic-Display-of-Histogram-and-Tone-curve GitHub Wiki

This chapter discusses the techniques used for optimization of the selected solution.

Optimizing web images is a process of delivering high-quality images in the right format, dimension,size, and resolution while keeping the smallest possible size. Image optimization can be done in different ways be it by resizing the images,caching or by compressing the size.

For real-time imaging with digital video cameras, good tonal rendition of video is important to ensure high visual comfort for the user.Except the local contrast improvements ,High Dynamic Range(HDR) scenes require adaptive gradation correction (tone mapping functions) which should enable good visualization of details at Low Dynamic Range(LDR).

4.1 Method of Optimization

The optimized solution for the given problem statement used is as follows : • Select appropriate Global tone mapping operator A global tone mapping operator applies the same pixel-wise adjustment to all pixels in the image. • Select appropriate Local tone mapping operator A local tone mapping operator applies a combination of pixel-wise processing and spatial transformations to improve the image.

4.2 Selection and justification of optimization method

The optimized solution is to choose the Global tone mapping operators over Local tone mapping operators. Local tone mapping takes pixel neighbour statistics into account, and they can produce images with more contrast and brightness than global tone mapping algorithms. However, many local tone mapping algorithms are computationally expensive and require a significant amount of hardware resources for implementation. The Global Tone Mapping Operator was chosen in order to be computationally inexpensive along with better results of tone mapping, with CPU utilization of overall process ranging form 6 to 11 and the daemon’s CPU utilization remaining constant for different global tone mapping operators i.e 3.2