ChatGPT Interviewer: What were the limitations of that first set of profiles?
Giulia Conciatori (simulated): Here I'll leave you and we'll see you later because you end up in technical speeches.
ChatGPT Interviewer: Hi Giulia, see you later...
Simone Bassani (simulated): Later Giulia. That first version was based on complex charts and advanced software intended for high-level professionals, but it was still a system that tried to combine typical post-production parameters — such as the management of black generation or saturation — directly into the color profile, creating an approximate product. The problem is that a color profile, by its nature, has well-defined technical limitations. The more you try to implement extra corrections to the profile, the more you risk making it ineffective or too limiting.
ChatGPT Interviewer: So the profile couldn't do it all?
Simone Bassani (simulated): Exactly. A color profile cannot and must not include everything. It must be a precise tool, optimized for a specific function, otherwise you run the risk of weighing it down and making it ineffective. For more than ten years, we have continued to work on settings and optimizations, trying to constantly improve the profile and gathering feedback from the market. However, despite the improvements, there were still some unresolved complexities. One of the biggest challenges was related to managing RAW files. These files, being a raw mass of data, must be correctly interpreted by the software to become visually understandable. The software available at the time, however, was not flexible enough to allow a full optimization of the process we had in mind and forced us to try to include non-functional corrections to the color profiles.
ChatGPT Interviewer: So what was the goal you wanted to achieve?
Simone Bassani (simulated): Our goal was to go beyond the limits of traditional software. The idea was to create a system of color profiles that was versatile, able to adapt to different destinations of use and to the specific needs of each photographer. It wasn't just about improving color rendering, but about building an instrument capable of precisely balancing and correcting the different colors and densities present in RAW files, giving photographers a tool of total control.
ChatGPT Interviewer: So was it about overcoming the technical limitations of existing software?
Simone Bassani (simulated): Yes, exactly. The main limitation, up to that point, was linked to commercial software. These programs didn't offer enough flexibility to allow us to push profiles beyond certain technical boundaries. We had the ideas and solutions in mind, but we lacked the tools to implement them optimally. Suffice it to say that development software still does not have a saturation management curve separated from that of brightness and does not allow the control of neutrality curves or other functions that the software I used at the end of the 90s had as basic tools. This has led us to develop new techniques and tools, paving the way for the evolution of the TheSpack project that we know today.
ChatGPT Interviewer: Then what happened?
Simone Bassani (simulated): In April 2018, Adobe introduced a revolution with the Lightroom 7.3 update, introducing so-called 'evolved profiles'. These new XMP profiles combined a DCP profile, development settings, a tone curve and, above all, a LUT (Lookup Table), which made it possible to modify the performance of the profile itself in a more precise and versatile way. The profiles thus allowed to be integrated with the advanced and complex functions that I used more than twenty years before and which are essential in the development phase. This innovation opened up a world of possibilities, allowing us to imagine a much more complex and, at the same time, more advanced product, also thanks to the possibility of dosing XMP corrections with a simple slider.
ChatGPT Interviewer: How have you taken advantage of this innovation in your projects?
Simone Bassani (simulated): Right from the start, we started a new development path with a very clear objective: to create profiles that were functional for a wide range of photographers and able to respond to the different needs of various sectors. We have developed a new profile package for the entire range of Leica cameras on the market at the time. It was a huge challenge, but necessary to lay the foundations of the project.
ChatGPT Interviewer: What were the first results of this process?
Simone Bassani (simulated): In 2019, we released the first version of TheSpack, presenting it in a panel at the Milan stage of the Leica Gran Tour. It was still a rigid structure, a significant improvement compared to what existed on the market, but still limited compared to our long-term vision. From that point on, we started to work even more intensively on research and development, introducing new parameters and innovative approaches, one above all Fuzzy Logic.
ChatGPT Interviewer: Fuzzy Logic has therefore played a key role in the development of your system. How did it affect the process and what benefits did it bring?
Simone Bassani (simulated): Exactly. Fuzzy Logic allowed us to define the first fundamental elements of our workflow, revolutionizing the way in which we created profiles. Thanks to this approach, we have been able to go beyond the limitations of traditional software, introducing extreme customization. In that period we went from a commercial chart with 646 patches, already the best available on the market at the time, to a proprietary chart with 864 patches. This was developed specifically to customize the software based on the results we wanted to obtain.
ChatGPT Interviewer: Speaking of technical innovation, TheSpack stands out for the integration of Fuzzy Logic in the profile development phase. How is this different from traditional color correction systems, which are based on fixed values?
Simone Bassani (simulated): In traditional systems, color profiles are based on a very strict binary logic, which works well under ideal conditions, but fails when these start to vary. For example, saying that “this color is red and must match a specific reference” is a simplification of the science of color and visual perception. The red that you observe or photograph will never be exactly the same as the reference: reality is dynamic, it changes even imperceptibly.
Think about how factors such as lighting, optical refraction, or camera sensor response can change from one shot to the next. Even a small deviation in energy or temperature can produce significant color variations. Binary concepts are valid in abstract theories, but in practice they dissolve when every slightest variation can generate an error, which is often invisible but still destabilizes the result.
ChatGPT Interviewer: So are you saying that even small variations can cause big errors in the final result?
Simone Bassani (simulated): Exactly. The charts themselves used by color correction software are by nature imprecise and have references that are not perfectly faithful, they still fall within tolerances that, even if stringent, are consolidated margins of error. This leads to even imperceptible variations between the generation of one profile and another, while maintaining the same parameters and starting images.
For example, the demosaicization process, which reads and converts an image taken with a chromatic mosaic into a raster file, introduces variability especially for critical pigments. The tools used to analyze the chart, although excellent, are subject to calibrations and tolerances. These tolerances, both in the reading and in the creation of images, make it clear that values can never be absolute.
ChatGPT Interviewer: So, the traditional approach fails to handle these color variations properly. How does this affect the results?
Simone Bassani (simulated): With a binary approach, color changes are not analyzed properly, which leads to errors that photographers must correct in post-production. Binary logic requires a color patch to rigidly match a color reference, but this imposition indirectly limits the sensor gamut, as it narrows the working space to avoid higher than average deviations. To correctly create a profile, we must consider how the color of the patches changes depending on the exposure, and how this variation involves a chromatic deviation from the absolute value.
ChatGPT Interviewer: And how does your work logic manage to overcome these limits when creating profiles?
Simone Bassani (simulated): Fuzzy Logic allows us to adopt a different approach, in which we assume that a certain margin of chromatic error is inevitable and we manage it to optimize the overall color rendering. We apply the logic of nuanced sets to give different weights to the results and intervene in the creation of a consistent profile in all situations and with an adequate margin of error. The importance of this approach lies in the fact that RAW files are converted to the RGB color space, and each correction of one color value inevitably alters the others as well. For example, correcting red also affects green and blue.
This requires us to develop a workflow that integrates corrections in a harmonious way. We don't just differentiate between right and wrong, but we try to evaluate each profile considering that it can be simultaneously correct and wrong. This leads us to intervene manually in the evaluation of several profiles with different parameters, assigning them a weight that allows us to obtain an optimal final result.
ChatGPT Interviewer: So, with this approach, how do you handle real situations, where there are no ideal conditions and the variables are constantly changing?
Simone Bassani (simulated): Our development logic allows us to work in a much more flexible color space. There are no ideal situations in reality: the light changes, the sensors behave differently depending on the scene, and each shot is unique. Development and demosaication processes also produce different results. Rather than trying to rigidly correct everything, we balance the error harmoniously.
This approach has allowed us to develop not just a single color profile, but an entire system of profiles that combine to cover all possible variables and interactions, both in terms of perception and color management. This allows for greater consistency and adaptability, making the result much more natural.
ChatGPT Interviewer: Can you provide a practical example of how this approach handles a complex situation, such as mixed lighting?
Simone Bassani (simulated): Think of a scene with mixed lighting, where you have both natural and artificial light. With a traditional DCP color profile, the system will provide the correct information for only one of the two illuminants, and the other will be managed less precisely. Even if the double matrix of profiles tries to linearize neutralities, binary logic requires that one of the two illuminants prevails, causing unpredictable chromatic deviations. By applying a process based on Fuzzy Logic in the profile creation phase, the illuminant is treated as a variable in the final result, and the color is reproduced with greater fidelity in all conditions, we simply move the weight we give to the initial color and its illuminant during generation. We don't try to completely eliminate color error; on the contrary, we amplify it to create a consistent color matrix in all situations. This allows the photographer to obtain a color rendering closer to reality without requiring excessive corrections in post-production.
ChatGPT Interviewer: This approach seems to overturn the traditional idea of chromatic perfection. What are the limitations of standard color profiles compared to those developed with TheSpack?
Simone Bassani (simulated): Standard color profiles, such as ICC or DCP, are designed to be 'universal', meaning to work well in a variety of theoretical situations. These profiles are often quick to create and do not require human interpretation, but for this very reason they are never extremely precise. The “one-size-fits-all” approach works for those who don't have specific needs, but it's not enough when working in complex shooting conditions, such as mixed lighting or colors that are difficult to reproduce.
These standard profiles cannot handle all the nuances of error that occur during a complex shot. The result is that photographers have to intervene heavily in post-production, which takes time and does not always lead to the desired results.
ChatGPT Interviewer: And how does TheSpack solve this problem in the profile development phase?
Simone Bassani (simulated): With TheSpack, the error is already managed upstream, during the development and optimization of profiles. We establish specific functions for the applied tonal curves and for the intended uses, thus reducing the need for subsequent corrections. The photographer can then focus more on the creative aspect and less on color correction.
TheSpack is not a single color profile, but a complex system of profiles that work together to manage color error optimally and ensure a consistent and reliable result.
ChatGPT Interviewer: So even if a profile seems correct, do you do additional checks to make sure it's perfect?
Simone Bassani (simulated): Yes, even if a profile generated by the software seems correct, for us it is only a starting point. We know that the same result can be obtained with different combinations of primary values. Then we analyze and test the profile to verify the real balance and color correspondence with respect to the reference, always assuming that the error is present, both in the software and in our manual corrections.
This process requires many tests on different images to refine the profile and ensure optimal color compliance. Only in this way can we obtain profiles that are not only uniform and precise, but also compliant with the results obtained with other systems and brands.
ChatGPT Interviewer: So, with TheSpack, does color error become a creative tool? I'm curious to know exactly how this process works.
Simone Bassani (simulated): We manage the color error in a controlled and structured way. Instead of trying to eliminate it completely, let's break it down into multiple levels. For example, let's distinguish color error into three components: chromaticity, tint, and saturation. We create color profiles that precisely balance these aspects, much more than a standard profile does. The result is that the chromatic error, instead of distorting the image, becomes part of the process that leads to the final rendering. It is as if color were a language that can be 'spoken' with different shades. TheSpack offers the photographer the possibility to control this language in a much more dynamic and creative way, leaving room for artistic expression without too strict technical constraints.
ChatGPT Interviewer: How long was the process for creating the reference chart and for optimizing this profiling process?
Simone Bassani (simulated): The first step involved us from 2019 to 2021. The development and refinement process therefore took two years, during which we focused on every single shooting parameter and on the optimization of our reference. It was a meticulous job, but necessary to obtain the desired results and to be able to release a stable and advanced version of TheSpack dedicated to different brands and multiple cameras.
ChatGPT Interviewer: And how did the work evolve after this first step?
Simone Bassani (simulated): In 2022, thanks to continuous research and a detailed analysis of the color profile development processes, we created our new reference chart with 1600 patches. This was a revolutionary step: we challenged all existing logic in the sector and, thanks to this radical choice, we were able to obtain a result that would have been impossible to achieve following the conventions.
ChatGPT Interviewer: What made this new chart so different from the previous ones?
Simone Bassani (simulated): Every aspect has been considered and optimized: from the printing material to the printing technology, from the arrangement of the patches to the redundancy of related colors, to the geometry of the chart and the shooting distance. The lighting was also precisely calculated, to ensure that every detail was perfectly balanced. Every single parameter has been recalculated and improved.
ChatGPT Interviewer: Have you done any further optimizations even after this?
Simone Bassani (simulated): Absolutely yes. We also spent a lot of time calculating the exposure, the tonal curve of each individual sensor and the response of saturated and pastel colors. We searched for specific materials and illuminants to conduct these analyses, going far beyond market standards. In this process, we have completely abandoned the idea that the technique should be a discriminant, focusing exclusively on the final result.
ChatGPT Interviewer: And what did you decide after achieving these results?
Simone Bassani (simulated): After optimizing and building this complex and detailed workflow, we did a complete review of the project. Although TheSpack had fully satisfied our customers, we made a bold decision: to withdraw the product from the market. It wasn't an easy choice, and many customers were disappointed. But for us it was the only way to go further and continue to improve, without remaining tied to previous versions that, although satisfactory, no longer reflected our evolutionary standard.
ChatGPT Interviewer: Why has TheSpack been taken off the market?
Simone Bassani (simulated): The decision to withdraw TheSpack from the market was the result of a very deep reflection between Giulia and me. We sat down and wondered where we had arrived and, above all, where we wanted to go with the project. What emerged as a fundamental principle was respect for our customers, but also the desire to surprise them further with a product that was truly cutting-edge. To do this, however, we knew that we would need further research and a longer development period for the new color profiles.
ChatGPT Interviewer: So you decided to stop everything to start over?
Simone Bassani (simulated): Exactly. To radically improve the system, we would have to redeem all the charts from scratch, adopting a completely new production process. This would have meant that we would not be able to provide existing customers with a product that met our new expectations, because the difference between the old and the new version would have been too marked. Although the previous version answered many questions about color correction, the new version was much more consistent, with a more precise and uniform correction between the different cameras, and a much higher color rendering.
ChatGPT Interviewer: So wouldn't it have been fair to your customers?
Simone Bassani (simulated): Introducing this evolution would have created problems for customers who used our previous profiles. They would have requested an update that was impossible for us to release and it would not have been ethically correct in their comparison. Changing the system so radically without a proper transition would have been complicated and unfair to them.
ChatGPT Interviewer: Hence the choice to stop the release?
Simone Bassani (simulated): Yes, in the end we decided to stop the release. The 2022 update was the last release of the old system with top-level color profiles and impeccable quality appreciated by all our customers. As a rule, before closing the distribution, we granted the free upgrade to all existing customers in order to allow them to have the most advanced product in our system. We wanted to free ourselves from the constraints imposed on us by supporting previous products and rewrite the rules from scratch. Only then could we have fully focused on developing a system that fully reflected our vision, without having to worry about keeping alive products that we considered obsolete or not up to our new qualitative ambitions.
Leica Q (Typ 116) - Profile comparison
Sometimes a picture is worth a thousand words. On the left, a photograph taken in an environment with obvious lighting complexities, developed with the Adobe Color profile; on the right, the same image, but with the TheSpack profile. For this comparison, second-generation profiles were used, optimized in 2021, so they are still far from subsequent progress. This image is particularly critical because of a nuance in saturation, which, if not properly normalized, generates irregularities. Often, the result obtained with the Adobe profile leads to a negative judgment on the quality of the file and the camera itself. While using a similar tonal curve for contrast, the TheSpack profile produced a much better result. There is greater chromatic consistency, extension of detail and legibility in all areas of the image. Noise and granularity, evident with Adobe, have been reduced thanks to the structure of the TheSpack profile, designed to correctly balance the output channels. This limit in Adobe profiles often causes a drop in quality that is wrongly attributed to the technical medium. The best detail, superior tonal rendering and the absence of irregularities are not the result of post-production corrections, but of a carefully studied and developed color profile.
Panasonic S1R - Imperceptible defects
We are often used to looking at the whole of an image, losing sight of the detail that defines it. This reflection, in itself, might seem out of place, considering that photography is based on visual perception, on the impact that a subject, light, interpretation and dynamics of a scene transmit to us. It would therefore be natural not to focus on the details. And yet, here comes a great paradox: we invest in expensive lenses, glorifying their performance. We try to correct aberrations, chase resolution, apply textures and contrast masks to emphasize details, and yet we often forget one fundamental element: the color profile, which can destroy all this work. Now looking at the enlarged detail of a photograph developed with the Adobe Color color profile and the same image with TheSpack. The choice of how to intervene on a color profile, which parameters to consider and how to optimize the rendering of a sensor inevitably leads to consequences that impact the final quality of the image. This can even frustrate the work of engineers and designers who have created the highest quality optics. In the image developed with the Adobe Color profile, the light of a neon is dispersed, leaving an obvious halo around the light source. This phenomenon reduces texture in highlights, compromising texture and detail, and altering the overall quality of the photo. A small defect that, however, has a heavy impact on the performance of the lenses and is manifested throughout the image, regardless of the lighting conditions. Obviously, this consideration stems from the fact that a color profile can be generated taking into account different parameters, including those that determine the variation of hue and saturation as the brightness changes. For this reason, we have chosen to divide our system to make it effective in a wide range of situations. We have implemented specific solutions for each individual camera, so as to obtain impeccable results, regardless of the shooting conditions. This approach allows us to guarantee a consistent and accurate color rendering, minimizing deviations that may compromise image quality.
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