Optimize for common cases

When implementing algorithms, create specialized versions for common cases to improve performance, while maintaining a generic version for completeness. For functions that process data with different channel counts, implement separate optimized code paths for 1-channel and 3-channel data, which represent the majority of image processing workloads.

For example, in histogram-based median filtering:

// Specialized version for 1-channel images
if (channels == 1) {
    uint16_t* hist0 = hist256[0].data();
    for (int x = x_start; x != x_end; x += x_step) {
        // Optimized single-channel histogram update
    }
}
// Specialized version for 3-channel images
else if (channels == 3) {
    uint16_t* hist0 = hist256[0].data();
    uint16_t* hist1 = hist256[1].data();
    uint16_t* hist2 = hist256[2].data();
    for (int x = x_start; x != x_end; x += x_step) {
        // RGB-specific histogram update
    }
}
// Generic version for other channel counts
else {
    for (int x = x_start; x != x_end; x += x_step) {
        // Generic multi-channel implementation
    }
}

Structure loops efficiently to avoid redundant calculations. When processing arrays, consider vectorization boundaries and ensure proper handling of edge cases with minimum conditional branches. For iterative algorithms with fixed-size vectors, implement a tail-handling pattern:

size_t i = 0;
for (; i <= len - vl; i += vl) {
    // Process full vector width
}
if (i < len) {
    size_t tail_len = remaining_elements(len - i);
    // Process remaining elements
}

This pattern avoids unnecessary modulo operations and extra variables for tracking remainders. When implementing algorithms with specialized branches, ensure that all cases are handled correctly, including those that don’t meet optimization criteria.