Images require contrast because it improves the visibility of details and decreases it in low contrast. A higher image quality is necessary to avoid the low-contrast effect, which can occur for several reasons. Appropriate handling of the low-contrast effect requires a quick method that maintains image details. The inability to control brightness, produce decent colors, provide enough contrast, or generate processing artifacts is a major flaw in many existing algorithms, leading to their failure. An original HLIPSCS algorithm employing hyperbolic, logarithmic, and statistical methods is detailed to avoid these limitations. Apply two hyperbolic procedures and then use a modified logarithmic method to integrate their outputs. Statistical as well as contrast stretching techniques are required to achieve the target outcome. HLIPSCS is applied on actual color and grayscale images. After that, it is assessed by comparing it to another algorithm called ABCETP. In all tests and comparisons, HLIPSCS performed the best in terms of image assessment. In general, HLIPSCS enhances color rendering, contrast, and brightness.