\chapter{Hypothesis \& Experimental Proof}
\label{chap:proof}
This chapter defines the core scientific contribution of this thesis.
It consists of a scientific hypothesis which is defined to address the main issues related to light field rendering.
These issues are currently not efficiently solved by existing state-of-the-art methods.
The hypothesis is followed by sections describing the experiments that were implemented as its proof.

\input{chapters/Hypothesis}

\input{chapters/LightFieldRendering}

\clearpage
\section{Hypothesis Proof Summary}
\label{sec:proofSummary}
The presented results complete the proof and solve the set goals.
The proof of the proposed \hyperref[hypothesis]{Hypothesis} not only supports the scientific achievements of the thesis, but also, perhaps more importantly, shows that the light field rendering can be practically used.
The proposed rendering method highlights the advantages and mitigates the disadvantages of light fields as much as possible to achieve better quality than existing methods and significant computational speedup.

Based on the results achieved and published, the proposed \hyperref[hypothesis]{Hypothesis} can be claimed as proved.
Each part of the hypothesis was proved as follows:
\begin{itemize}
    \item \textit{A novel method of light field rendering can be designed and implemented...} \\
    A reference implementation of the proposed method was created and used in the experiments.
    The novelty of the method was proved by peer-reviewed publications and acceptance by the scientific community.
    \item \textit{...that produces all-focused renders directly from input views without exploiting additional depth information,...} \\
    The method is capable of automatically detecting the best focus distance for each pixel of the result, as described in Section~\ref{sec:perpi}.
    The result is all-focused, with minimum visible out-of-focus artifacts.
    The proposal works only with the input images, so no structural information about the scene is necessary, as described in Section~\ref{sect:realtime}.
    The necessary focus range of the scene can be detected automatically, as shown in Section~\ref{sec:improvFocus}.
    \item \textit{...having better visual quality, being computationally more efficient, and having less memory requirements compared to the state of the art.} \\
    The experimental evaluation of the proposal in Sections~\ref{sec:evalStand} and~\ref{sec:eval4K} shows that the proposed method achieves better visual quality, higher computational efficiency, and less demanding memory requirements than state-of-the-art methods. 
\end{itemize}    

The experimental measurements were conducted on standard datasets and also on a novel original dataset.
It is possible that certain data might not be suitable for the proposed method. 
However, the tested datasets cover most of the scenes commonly used in both industrial and real-life applications. 
Therefore, it can be assumed that the proposal can generally achieve optimal results.    
    
On top of the hypothesis proof, the following light field rendering advantages, mentioned in Chapter~\ref{chap:lf}, are highlighted in the proposed method:
\begin{itemize}
    \item \textbf{Constant render time}\\
    The proposed method is based on scanning of the focus range that consists of a fixed number of steps; see Section~\ref{sec:perpi}.
    The most time-consuming part of each step is the texture read operation, which can also be considered constant-time.
    This ensures a constant render time, which depends only on the light field resolution or focusing quality settings.    
    \item \textbf{Constant memory requirements}\\
    The method uses only the nearest views of the light field grid; see Section~\ref{sec:parameters}.
    All necessary memory allocations can be made in advance and no reallocations are necessary.
    \item \textbf{Rendering algorithm independent of scene content}\\
    The previous two points show that the algorithm performs the same operations on all types of data.
    The camera position change only affects the selection of the necessary views of the light field grid.
    The measurements carried out showed that the method is robust and works well for existing standard and novel original datasets; see Sections~\ref{sec:evalStand} and~\ref{sec:eval4K}.
    It can be assumed that the method would work for most potential scene types independently of their content.
    \item \textbf{Simple representation as textures}\\
    The method adopted the widely used format of discrete light field approximation as a set of images.
    The standard datasets can be used directly without any preprocessing; see Section~\ref{sec:evalStand}.
    \item \textbf{Straightforward acquisition}\\
    No changes are necessary in the acquisition process.
    A simple capture of the scene in a predefined grid suffices.
    The proposed optimal acquisition method follows the same principles and only affects the positions of the cameras; see Section~\ref{sec:novelCapture}.
    However, the method works even for data that were captured without the proposed guidelines, although not necessarily achieving the highest possible quality.
    \item \textbf{Easily implementable on parallel architectures}\\
    Light field rendering is expected to be used primarily on GPU to make it available in graphic applications.
    The proposed method is designed to fully utilize GPU parallelism.
    The algorithm is not difficult to implement, compared to alternative rendering methods that include various algorithms for specific material or lighting settings.
\end{itemize}

In addition, the following most crucial disadvantages of the typical light field rendering methods are mitigated by the proposal:
\begin{itemize}
    \item \textbf{Excessive memory requirements}\\
    Compared to previous methods, which require the entire light field grid to be present in the GPU memory, the proposed method only requires the four closest views to be used in the algorithm; see Section~\ref{sec:eval4K}.
    Other allocations, such as the focus map image, can even be downscaled without a significant quality loss; see Section~\ref{sec:evalStand}.
    Also, no pre-trained deep-learning weights are necessary to be uploaded to the GPU memory.
    \item \textbf{Excessive streaming requirements}\\
    The proposed method is compatible with the proposed compression method based on GPU-accelerated video decoders; see Section~\ref{sec:compression}.
    These decoders proved to be the optimal choice for the streaming of light field data.
    \item \textbf{Focusing artifacts}\\
    The experimental evaluation of the proposed method showed that a high-quality, all-focused image can be produced as a result; see Sections~\ref{sec:evalStand} and \ref{sec:eval4K}.
    Certain artifacts can still be visible, but the quality of the result outperforms the majority of state-of-the-art approaches.
    Furthermore, the possible artifacts might not be clearly visible, as the scene might be focused at a distance which is near the correct one or focused somewhere else where the color similarity metric ensures that the part of the image looks natural.
    \item \textbf{Time consuming reconstruction with missing structural information}\\
    The inputs required for the proposed method are the input images and the aspect ratio of the capturing cameras.
    The method does not require any depth information as input; see Section~\ref{sect:realtime}.
    Other methods use, for example, depth maps, which might not always be available, their estimation would slow down the process, and their usage would increase the memory requirements.
    No heavyweight 3D reconstructions, data preprocessing, or camera intrinsic parameters are necessary to produce an all-focused view in real time.
    In addition, no deep learning training process is necessary prior to using the method.
    \item \textbf{Limited view space}\\
    The proposal focuses on the best quality interpolation of the views in the space enclosed by the input light field grid.
    Possible extrapolations outside of the grid are beyond the scope of this thesis.
    The virtual camera can freely move inside the scene bounding box defined by positions of the input cameras.
\end{itemize}

Experimental measurements revealed that the method outperforms other state-of-the-art ones.
In combination with the proposed compression scheme and GPU accelerated algorithms, the method seems to be an optimal choice for a real-time light field rendering.
It can be assumed that \hyperref[hypothesis]{Hypothesis} was proved in the experiments.

The proposed methods were implemented in several experimental applications that were used for the measurements.
All applications serve as proof-of-concept which can be used after necessary refactoring in industrial scenarios.
All resources are available online\footnote{\href{https://www.fit.vutbr.cz/~ichlubna/research}{fit.vutbr.cz/$\sim$ichlubna/research}} and are free to use for other researchers or developers.
