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-\chapter{Discussion}
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+\chapter{Discussion}
+
+This chapter interprets the results obtained in the benchmarking experiments, placing them in a broader theoretical and practical context.Explores what the results imply about the efficiency, ease of implementation, licensing concerns, and usability of the evaluated image processing libraries. Furthermore, addresses the larger implications of these findings for software development and image processing as a field.
+
+\section{Interpreting the Results: Performance vs. Practicality}  
+
+The results obtained from our benchmarking study reveal a clear hierarchy of performance among the tested libraries. However, performance alone does not determine the best library for a given use case. The ideal choice depends on a variety of factors, including memory efficiency, ease of integration, licensing constraints, and the specific needs of the application.  
+
+\subsection{Performance Trade-offs and Suitability for Real-World Applications}
+
+From performance standpoint, OpenCvSharp + SkiaSharp and Emgu CV + Structure.Sketching outperform ImageSharp in both image conversion and pixel iteration tasks. However, these libraries require more complex implementations compared to ImageSharp’s user-friendly API. While ImageSharp is slower, it remains a compelling option for projects where ease of use is prioritized over raw speed. SkiaSharp, with its lightweight architecture and cross-platform compatibility, demonstrated remarkable performance in image conversion tasks. It consistently outperformed ImageSharp while consuming significantly less memory. This makes SkiaSharp an ideal choice for applications requiring efficient format conversion without extensive manipulation of individual pixels. Emgu CV, despite its high memory usage, proved to be the fastest option for pixel iteration. This is unsurprising, given its reliance on OpenCV’s highly optimized C++ backend. However, its higher memory footprint may be a drawback for applications running on constrained systems.  Magick.NET, on the other hand, performed well in certain tasks but fell short in pixel iteration due to excessive processing times. This suggests that while Magick.NET is a robust tool for high-quality image manipulation and format conversion, it may not be suitable for performance-critical applications requiring low-latency processing. in graph \ref{fig:image-conversion} and \ref{fig:pixel-iteration} the performance comparison of the libraries in image conversion and pixel iteration tasks respectively can be seen.
+
+\subsection{The Impact of Licensing on Library Selection}  
+
+Licensing can be a key consideration in selecting an image processing library. The cost of proprietary solutions can be prohibitive, particularly for small businesses or open-source projects. ImageSharp, while powerful, requires a yearly cost of couple of thousand dollars for commercial use.This cost must be weighed against its performance limitations. Open-source alternatives like OpenCvSharp and SkiaSharp, which are licensed under MIT and Apache 2.0 respectively, offer a compelling alternative by providing high performance at no cost. Emgu CV, although based on the open-source OpenCV framework, requires a one-time fee (version specific) of less than thousand dollars, with additional costs for future upgrades. While this is significantly more affordable than ImageSharp, it still represents an investment that must be justified by superior performance. on the other hand,Magick.NET was licensed under Apache 2.0, and provides extensive functionality for free, making it an attractive option for projects that require advanced image processing features but cannot afford proprietary licenses.  
+ 
+\begin{longtable}
+    {|>{\raggedright\arraybackslash}p{0.30\textwidth}|>{\raggedright\arraybackslash}p{0.20\textwidth}|>{\raggedright\arraybackslash}p{0.20\textwidth}|>{\raggedright\arraybackslash}p{0.20\textwidth}|}
+    \hline
+    \rowcolor{purple!30}
+    \textbf{Library Combination} & \textbf{Licensing Model} & \textbf{Cost} & \textbf{Usage Restrictions / Remarks} \\
+    \hline
+    \endfirsthead
+
+    \hline
+    \rowcolor{purple!30}
+    \textbf{Library Combination} & \textbf{Licensing Model} & \textbf{Cost} & \textbf{Usage Restrictions / Remarks} \\
+    \hline
+    \endhead
+
+    \textbf{ImageSharp} & Proprietary (Commercial) & ~\$5,000/year & Requires a subscription; higher conversion times \\\hline
+    \textbf{OpenCvSharp + SkiaSharp} & Open-source (Apache-2.0 \& MIT) & Free & No recurring fees; excellent conversion performance \\\hline
+    \textbf{Magick.NET} & Open-source (Apache-2.0) & Free & Good for advanced processing; slower pixel iteration \\\hline
+    \textbf{Emgu CV + Structure.Sketching} & Open-source with paid tier & ~\$799 (Emgu CV only) & Cost-effective; strong for pixel manipulation and processing \\\hline
+
+    \caption{Library Licensing, Costs, and Usage Restrictions Comparison Table}
+    \label{tab:licensing}
+\end{longtable}
+
+
+\section{Strengths and Weaknesses of the Different Libraries}  
+
+ImageSharp’s biggest advantage is its simple API and pure .NET implementation. It is easy to integrate and requires minimal setup. However, our benchmarks show that it lags behind other libraries in performance. Its relatively high memory efficiency during pixel iteration is a plus, but for tasks requiring fast image conversion or pixel-level modifications, other options are preferable.  
+OpenCvSharp+SkiaSharp: High Performance, Moderate Complexity.This combination provides the best balance between speed and memory efficiency. OpenCvSharp offers the power of OpenCV’s optimized image processing, while SkiaSharp enhances its rendering and format conversion capabilities. However, using these libraries effectively requires familiarity with both OpenCV and SkiaSharp APIs, making them less beginner-friendly than ImageSharp. Emgu CV’s performance in pixel iteration tasks is unmatched, making it ideal for applications involving real-time image analysis, such as AI-driven image recognition. However, its high memory consumption may pose a problem for resource-limited environments. Structure.Sketching complements Emgu CV by providing efficient image creation and drawing capabilities, making this combination well-suited for applications requiring both processing speed and graphical rendering.  In contrast,Magick.NET excels in high-quality image manipulation and resampling but falls short in raw speed. The high processing times recorded for pixel iteration indicate that Magick.NET is best suited for batch processing or scenarios where quality takes precedence over execution time. And MagickScaler, provides advanced image scaling capabilities, making it a valuable tool for applications requiring precise image resizing and enhancement.
+
+Overally There is no single library that is best for all use cases. The optimal choice depends on the application’s specific requirements. If ease of implementation and maintainability are priorities, ImageSharp remains a solid choice despite its performance drawbacks. For performance-intensive applications where raw speed is essential, OpenCvSharp+SkiaSharp or Emgu CV+Structure.Sketching are superior choices.  
+  
+\vspace{1em}
+\includegraphics[width=\textwidth]{media/usecase.png}
+\captionof{figure}{Mapping different libraries to their ideal use cases}
+\label{fig:usecase}
+
+\section{Considerations for Future Research}  
+
+Image processing is a fundamental component of many industries, including medical imaging, computer vision, digital content creation, and web applications. The performance gains demonstrated by OpenCvSharp and Emgu CV suggest that these libraries can benefit a wide range of applications, from autonomous vehicle navigation to medical diagnostics.  
+
+Moreover, the balance between speed and memory efficiency is a recurring challenge in computational imaging. This study highlights the need for hybrid approaches—such as combining OpenCvSharp with SkiaSharp to achieve optimal performance while minimizing resource consumption.  
+
+Future research could explore the following areas to further enhance the capabilities of image processing libraries:
+
+\textbf{Expanding the Scope of Benchmarking:} While our study focused on image conversion and pixel iteration, real-world applications often require additional operations such as filtering, blending, and object detection. Future research could expand the benchmarking scope to include these tasks, providing a more comprehensive evaluation of each library’s capabilities.  
+
+\textbf{GPU Acceleration and Parallel Processing:} One limitation of our study is that all benchmarks were conducted on a CPU. Many modern image processing tasks benefit from GPU acceleration, which libraries like OpenCV support. Investigating the performance of these libraries on GPU-accelerated hardware could yield valuable insights into their scalability and efficiency.  
+
+\textbf{Cloud-Based Processing:} With the growing adoption of cloud computing, it would be beneficial to evaluate how these libraries perform in cloud-based environments such as AWS Lambda or Azure Functions. Factors such as cold start times, scalability, and integration with cloud-based storage solutions would be critical considerations for enterprise applications.  
+
+\textbf{Further Optimizations in Memory Usage:} Although Emgu CV was the fastest in pixel iteration, its high memory consumption remains a concern. Future research could explore memory optimization techniques, such as reducing redundant data structures or leveraging memory-efficient algorithms, to improve its efficiency without compromising speed.  
+
+\section{Closing Thoughts}  
+
+The findings of this study offer clear guidance for developers seeking to optimize their image processing workflows. While ImageSharp remains a user-friendly option, open-source alternatives such as OpenCvSharp and SkiaSharp provide superior performance at no cost. Emgu CV excels in computationally intensive tasks but requires careful memory management, while Magick.NET remains a powerful tool for applications prioritizing high-quality output.  
+
+Ultimately, the choice of an image processing library should be guided by the specific needs of the application. Whether prioritizing speed, memory efficiency, ease of integration, or licensing freedom, developers now have a well-defined framework for making informed decisions.  
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