In the world of online boat marketplaces, the user experience can make or break a sale. A beautiful yacht with poor quality photos might sit unsold, while an average boat with stunning visuals attracts multiple buyers. At SINAPTIA, we recently tackled this exact challenge for a leading boat marketplace, transforming thousands of low-quality images using AI-powered upscaling technology.
The problem
The marketplace we work with faces a common issue in the industry: image quality varies dramatically across listings. While some boat owners upload high-resolution, professional photos, many images come from third-party sources or older listings with significantly lower quality. The platform works with millions of images per month, with the vast majority coming from automatically imported external sources.
This creates several challenges:
- Poor user experience: Potential buyers can’t properly evaluate boats with pixelated or blurry images
- Reduced conversions: Low-quality images directly impact the likelihood of inquiry or sale
- Mobile optimization issues: Most users browse on mobile devices, where image quality is even more critical
- Competitive disadvantage: Listings with better images naturally perform better in search results
The solution
Rather than asking users to re-upload better images (often impossible with imported listings) or manually editing thousands of photos, we implemented an AI-powered solution that automatically enhances image quality.
Choosing the right technology
We evaluated several approaches before settling on AI-based upscaling:
Traditional upscaling methods rely on mathematical algorithms to interpolate new pixels based on existing image data. These include techniques like nearest-neighbor interpolation, bicubic interpolation, and Lanczos resampling. While these methods vary in sophistication and computational requirements, they share a fundamental approach: they analyze existing pixels and use mathematical formulas to estimate what new pixels should look like.
The core problem with traditional methods is that they can increase image dimensions but cannot add new visual information. A 400-pixel image scaled to 800 pixels using these methods will occupy more space but won’t look significantly better. It may even appear worse due to artifacts produced by the pixel manipulation.
Diffusion models can intelligently add detail and texture during the scaling process. AI-based upscaling leverages trained neural networks that have learned patterns from millions of high-quality images. Rather than simply interpolating existing pixels, these models can generate new visual information that makes logical sense within the image’s context. This means they can enhance textures, sharpen details, and add realistic elements that weren’t clearly visible in the original low-resolution version.
Candidate image selection criteria
Determining which images to process required careful analysis of cost versus benefit. We established specific criteria for candidate selection:
Resolution Thresholds: Images with width or height dimensions between 200 and 800 pixels were considered optimal candidates. Images below 200 pixels produced poor results regardless of the algorithm used, while images above 800 pixels already provided adequate quality for our use cases.
Cost Considerations: AI-powered upscaling is computationally expensive. With approximately 30,000 new images entering our system daily, we needed to balance image quality improvements with operational costs. Processing every image would have been prohibitively expensive.
Device Optimization: Since most of our users browse on mobile devices where the largest displayed image is approximately 800 pixels wide, scaling beyond 2x didn’t provide meaningful benefits and would have increased processing costs and bandwidth usage unnecessarily.
After extensive testing with various pixel ranges and manual quality evaluation, we found that approximately 5% of our images met the criteria for AI upscaling; a manageable volume that provided significant quality improvements where they were most needed.
Results and implementation
Choosing the right AI model required extensive testing and evaluation. We began by creating a curated dataset of representative boat images with various quality issues: images that reflected the real-world problems we needed to solve. This dataset became our benchmark for comparing different models, providers, and configurations.
The evaluation process was inherently manual and time-intensive. Since image quality is subjective and directly impacts user experience, we couldn’t rely on automated metrics or other AI models to determine what “looked better.” Human evaluation was essential—we needed actual people to examine the processed images and assess whether the AI had successfully enhanced the photos or had introduced unwanted artifacts.
Through this iterative testing process, we discovered that many high-end models designed for human faces or fine art restoration were overkill for our use case. Boat photography proved more forgiving than portrait photography, allowing us to achieve excellent results with more cost-effective models. We ultimately selected an ESRGAN model that offered the optimal balance of quality improvement and processing cost.
The implementation was designed to run independently from our existing image processing pipeline, maintaining flexibility for future modifications while avoiding disruption to established workflows. This approach, while requiring additional storage for multiple image versions, provided the modularity needed for ongoing optimization and experimentation.
Conclusion
AI-powered image upscaling has proven to be an effective solution for improving user experience in our client’s marketplace. By carefully selecting candidate images based on resolution thresholds and cost-benefit analysis, we enhanced the visual quality of thousands of boat listings without overwhelming operational costs.
The key to success was understanding that not every image was worth enhancing; strategic application based on clear criteria delivers maximum impact. For platforms dealing with user-generated content or third-party image sources, AI upscaling represents a powerful tool for maintaining visual quality standards while preserving the scalability needed for large-volume operations.
At SINAPTIA, we specialize in helping businesses implement AI solutions that deliver real value. If you’re facing similar challenges with large-scale data processing, content enhancement, or other AI applications, we’d love to help you explore what’s possible.