+Computer graphics comes in two forms: bit-mapped (or raster) graphics, which is defined by an array of pixel colours;
+and \emph{vector} graphics, defined by mathematical descriptions of objects. Bit-mapped graphics are well suited to photographs
+and are match how cameras, printers and monitors work. However, bitmap devices do not handle zooming beyond their
+``native'' resolution --- the resolution where one document pixel maps to one display pixel ---, exhibiting an artefact
+called pixelation where the pixel structure becomes evident. Attempts to use interpolation to hide this effect are
+never entirely successful, and sharp edges, such as those found in text and diagrams, are particularly affected.
+
+\begin{figure}[h]
+ \centering \includegraphics[width=0.8\linewidth]{figures/vectorraster_example}
+ \caption{A circle as a vector image and a $32 \times 32$ pixel raster image}
+\end{figure}
+
+
+Vector graphics lack many of these problems: the representation is independent of the output resolution, and rather
+an abstract description of what it is being rendered, typically as a combination of simple geometric shapes like lines,
+arcs and ``B\'ezier curves''\cite{catmull1974asubdivision}.
+As existing displays (and printers) are bit-mapped devices, vector documents must be \emph{rasterized} into a bitmap at
+a given resolution. This bitmap is then displayed or printed. The resulting bitmap is then an approximation of the vector image
+at that resolution.
+
+This project will be based around vector graphics, as these properties make it more suited to experimenting with zoom
+quality.
+
+
+The rasterization process typically operates on an individual ``object'' or ``shape'' at a time: there are special algorithms
+for rendering lines\cite{bresenham1965algorithm}, triangles\cite{giesen2013triangle}, polygons\cite{pineda1988parallel} and B\'ezier
+Curves\cite{goldman_thefractal}. Typically, these are rasterized independently and composited in the bitmap domain using Porter-Duff
+compositing\cite{porter1984compositing} into a single image. This allows complex images to be formed from many simple pieces, as well
+as allowing for layered translucent objects, which would otherwise require the solution of some very complex constructive geometry problems.
+
+While traditionally, rasterization was done entirely in software, modern computers and mobile devices have hardware support for rasterizing
+some basic primitives --- typically lines and triangles ---, designed for use rendering 3D scenes. This hardware is usually programmed with an
+API like \texttt{OpenGL}\cite{openglspec}.
+
+More complex shapes like B\'ezier curves can be rendered by combining the use of bitmapped textures (possibly using signed-distance
+fields\cite{leymarie1992fast}\cite{frisken2000adaptively}\cite{green2007improved}) with polygons approximating the curve's shape\cite{loop2005resolution}\cite{loop2007rendering}.
+
+Indeed, there are several implementations of entire vector graphics systems using OpenGL: OpenVG\cite{robart2009openvg} on top of OpenGL ES\cite{oh2007implementation};
+the Cairo\cite{worth2003xr} library, based around the PostScript/PDF rendering model, has the ``Glitz'' OpenGL backend\cite{nilsson2004glitz} and the SVG/PostScript GPU
+renderer by nVidia\cite{kilgard2012gpu} as an OpenGL extension\cite{kilgard300programming}.
+
+
+\section{Numeric formats}
+
+On modern computer architectures, there are two basic number formats supported:
+fixed-width integers and \emph{floating-point} numbers. Typically, computers
+natively support integers of up to 64 bits, capable of representing all integers
+between $0$ and $2^{64} - 1$, inclusive\footnote{Most machines also support \emph{signed} integers,
+which have the same cardinality as their \emph{unsigned} counterparts, but which
+represent integers in the range $[-(2^{63}), 2^{63} - 1]$}.
+
+By introducing a fractional component (analogous to a decimal point), we can convert
+integers to \emph{fixed-point} numbers, which have a more limited range, but a fixed, greater
+precision. For example, a number in 4.4 fixed-point format would have four bits representing the integer
+component, and four bits representing the fractional component:
+\begin{equation}
+ \underbrace{0101}_\text{integer component}.\underbrace{1100}_\text{fractional component} = 5.75
+\end{equation}
+
+
+Floating-point numbers\cite{goldberg1992thedesign} are the binary equivalent of scientific notation:
+each number consisting of an exponent ($e$) and a mantissa ($m$) such that a number is given by
+\begin{equation}
+ n = 2^{e} \times m
+\end{equation}
+
+The IEEE 754 standard\cite{ieee754std1985} defines several floating-point data types
+which are used\footnote{Many systems' implement the IEEE 754 standard's storage formats,
+but do not implement arithmetic operations in accordance with this standard.} by most
+computer systems. The standard defines 32-bit (8-bit exponent, 23-bit mantissa, 1 sign bit) and
+64-bit (11-bit exponent, 53-bit mantissa, 1 sign bit) formats\footnote{The 2008
+revision to this standard\cite{ieee754std2008} adds some additional formats, but is
+less widely supported in hardware.}, which can store approximately 7 and 15 decimal digits
+of precision respectively.
+
+Floating-point numbers behave quite differently to integers or fixed-point numbers, as
+the representable numbers are not evenly distributed. Large numbers are stored to a lesser
+precision than numbers close to zero. This can present problems in documents when zooming in
+on objects far from the origin.
+
+IEEE floating-point has some interesting features as well, including values for negative zero,
+positive and negative infinity, the ``Not a Number'' (NaN) value and \emph{denormal} values, which
+trade precision for range when dealing with very small numbers. Indeed, with these values,
+IEEE 754 floating-point equality does not form an equivalence relation, which can cause issues
+when not considered carefully.\cite{goldberg1991whatevery}
+
+There also exist formats for storing numbers with arbitrary precising and/or range.
+Some programming languages support ``big integer''\cite{java_bigint} types which can
+represent any integer that can fit in the system's memory. Similarly, there are
+arbitrary-precision floating-point data types\cite{java_bigdecimal}\cite{boost_multiprecision}
+which can represent any number of the form
+\begin{equation}
+ \frac{n}{2^d} \; \; \; \; n,d \in \mathbb{Z} % This spacing is horrible, and I should be ashamed.
+\end{equation}
+These types are typically built from several native data types such as integers and floats,
+paired with custom routines implementing arithmetic primitives.\cite{priest1991algorithms}
+These, therefore, are likely slower than the native types they are built on.
+
+While traditionally, GPUs have supported some approximation of IEEE 754's 32-bit floats,
+modern graphics processors also support 16-bit\cite{nv_half_float} and 64-bit\cite{arb_gpu_shader_fp64}
+IEEE floats. Note, however, that some parts of the GPU are only able to use some formats,
+so precision will likely be truncated at some point before display.