Visual perception of screen elements depends on their color, font, and position in the user interface design. Objects in the central part of the screen are perceived more easily than those in the peripheral areas. However, the peripheral space is valuable for applications like advertising and promotion and should not be overlooked. Optimizing the design of elements in this area can improve user attention to peripheral visual stimuli during focused tasks. This study aims to evaluate how different combinations of text and background color affect the visibility of moving textual stimuli in the peripheral areas of the screen, while attention is focused on a central task. This study investigates how background color, combined with white or black text, affects the attention of participants. It also identifies which background color makes a specific word most noticeable in the peripheral part of the screen. We designed quizzes to present stimuli with black or white text on various background colors in the peripheral regions of the screen. The background colors tested were blue, red, yellow, green, white, and black. While saturation and brightness were kept constant, the color tone was varied. Among ten combinations of background and text color, we aimed to determine the most noticeable combination in the peripheral part of the screen. The combination of white text on a blue background resulted in the shortest detection time (1.376 s), while black text on a white background achieved the highest accuracy rate at 79%. The results offer valuable insights for improving peripheral text visibility in user interfaces across various visual communication domains such as video games, television content, and websites, where peripheral information must remain noticeable despite centrally focused user attention and complex viewing conditions.

Responsive web interfaces often require multiple renditions of each source image to support display across desktop, tablet, and smartphone clients. The source image loading method used in rendition generation can significantly affect processing time and resource usage, particularly on resource-constrained hardware. This study benchmarks six PHP/Imagick pipelines on a Raspberry Pi 4 Model B, differing in how the source JPEG is loaded and used to generate six renditions. The pipelines range from a baseline that decodes the full-resolution source image for each rendition to strategies combining Discrete Cosine Transform (DCT)-domain downsampling with in-memory cloning or memory-mapped Magick Pixel Cache (MPC) files. Each pipeline was evaluated on 12 high-resolution source images, with every image processed over 10 rounds, measuring execution time, CPU utilization, peak memory usage (resident set size, RSS), and CPU temperature. Computational cost was expressed as total CPU time. Output quality was assessed using Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) metrics, comparing renditions from each optimized pipeline against those produced by the baseline. Pipelines combining DCT-domain downsampling with a single-decode strategy achieved an approximately $4\times$ speedup and more than 62% lower peak RSS than the baseline, while producing renditions with LPIPS values below 0.05 and SSIM values above 0.95 for every tested image. Across all DCT-based pipelines, SSIM fell below 0.95 in only three observations, all from a single structurally complex source image. The presented strategies require minimal implementation effort and deliver significant gains on resource-constrained hardware.