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 \chapter{Discussion}
 
-\section{Interpretation of the Results}
+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.
 
-The benchmark results reveal clear performance differentials among the evaluated libraries. In particular, the image conversion tests showed that alternatives such as OpenCvSharp combined with SkiaSharp, and Emgu CV paired with Structure.Sketching, drastically reduce conversion times compared to ImageSharp. For example, while ImageSharp required 2754 ms for image conversion, the Emgu CV + Structure.Sketching combination achieved the task in just 490 ms. Similarly, pixel iteration tests indicated that Emgu CV yielded an average iteration time close to that of ImageSharp but with a significant advantage in cost, despite slightly higher memory usage.
+\section{Interpreting the Results: Performance vs. Practicality}  
 
-These results mean that for core operations—loading an image, converting formats, and iterating through pixels—there are measurable improvements in speed and efficiency when using alternative libraries. The reduced processing times translate directly into enhanced responsiveness for applications that rely on image processing, making them highly relevant for performance-critical systems.
+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.  
 
-\section{Conclusions and Comparative Strengths}
+\subsection{Performance Trade-offs and Suitability for Real-World Applications}
 
-The evidence leads to several key conclusions:
-\begin{itemize}
-    \item \textbf{No Universally Best Library:} There is no single library that outperforms others across every metric. Each alternative presents a distinct balance of speed, memory consumption, integration effort, and cost.
-    \item \textbf{Emgu CV Strengths and Weaknesses:} Emgu CV excels in handling complex image processing tasks, particularly in pixel iteration, where it achieved one of the fastest mean times. Its performance advantage is balanced by higher memory usage during intensive operations. Moreover, its cost-effective licensing (a one-time fee versus ImageSharp’s recurring cost) makes it appealing for long-term applications.
-    \item \textbf{SkiaSharp Strengths and Weaknesses:} SkiaSharp demonstrates superior performance in image conversion tasks, with very low memory allocation and rapid processing times. However, its pixel manipulation capabilities are more basic compared to those of Emgu CV, suggesting it is best suited as a complement to another library that handles more intensive processing.
-    \item \textbf{Alternative Combinations:} The combination of Emgu CV and SkiaSharp appears to provide the best overall trade-off. Emgu CV covers the low-level, performance-critical aspects of image processing, while SkiaSharp offers robust, high-quality 2D rendering and efficient format conversion.
-\end{itemize}
+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.
 
-These conclusions confirm that while no library is universally optimal, the right combination can cover a broader range of requirements while addressing specific weaknesses inherent in any single solution.
+\subsection{The Impact of Licensing on Library Selection}  
 
-\section{Relevance to the Larger Field}
+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
 
-The findings are relevant not only to the specific context of replacing ImageSharp in commercial applications but also to the broader domain of cost-effective and efficient image processing. In an era where performance and cost savings are critical—especially for web services, mobile apps, and enterprise solutions—the ability to reduce licensing fees without sacrificing processing speed is of significant importance. Furthermore, these results contribute to ongoing discussions in the image processing community about the trade-offs between open-source alternatives and commercial solutions.
+    \hline
+    \rowcolor{purple!30}
+    \textbf{Library Combination} & \textbf{Licensing Model} & \textbf{Cost} & \textbf{Usage Restrictions / Remarks} \\
+    \hline
+    \endhead
 
-\section{Implications for Further Research, Theory, and Practice}
+    \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
 
-\subsection{Implications for Practice}
-\begin{itemize}
-    \item \textbf{Adoption of Hybrid Solutions:} Practitioners can benefit from a hybrid approach —using Emgu CV for intensive processing and SkiaSharp for rendering— to optimize performance and cost.
-    \item \textbf{Cost Savings:} The significant reduction in recurring expenses (as compared to ImageSharp) can free up resources for other critical development activities.
-\end{itemize}
+    \caption{Library Licensing, Costs, and Usage Restrictions Comparison Table}
+    \label{tab:licensing}
+\end{longtable}
 
-\subsection{Implications for Further Research}
-\begin{itemize}
-    \item \textbf{Extended Benchmarking:} Future studies could expand the benchmarking metrics to include additional operations such as asynchronous processing, multi-threading, and GPU acceleration. Evaluating real-world workload scenarios would also help refine the performance comparisons.
-    \item \textbf{Integration Complexity:} Investigating the ease of integration and maintainability over long-term projects would provide further insights into the practical challenges of deploying these libraries in diverse environments.
-    \item \textbf{User-Centered Evaluations:} Beyond raw performance metrics, future research might consider end-user impact, including subjective assessments of responsiveness and quality, to complement the quantitative analysis.
-\end{itemize}
 
-\subsection{Methodological Considerations}
-The approach chosen for this study—focusing on image conversion and pixel iteration metrics—proved effective for isolating core performance aspects. However, incorporating a broader set of benchmarks (such as memory overhead in multi-threaded contexts or performance under varying image resolutions) would provide a more comprehensive view of each library's capabilities. Additionally, further studies could consider the evolution of these libraries over time, as continuous improvements may shift the balance of strengths and weaknesses.
+\section{Strengths and Weaknesses of the Different Libraries}  
 
-\section{Summary of the Discussion}
+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.
 
-In summary, the results indicate that while no single library is perfect, the combination of Emgu CV and SkiaSharp offers a compelling alternative to ImageSharp by delivering superior performance and cost advantages. The strengths of each library complement one another, and the findings have broad implications for cost-sensitive, performance-critical image processing applications. Future research can build upon this work by exploring additional metrics, integration challenges, and user experience factors, further refining our understanding of how to best meet the diverse needs of modern image processing tasks.
+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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