Function chess_response_u8 

pub fn chess_response_u8(
    img: &[u8],
    w: usize,
    h: usize,
    params: &ChessParams,
) -> ResponseMap

Compute the dense ChESS response for an 8-bit grayscale image.

The response at each valid pixel center is computed from a 16‑sample ring around the pixel and a 5‑pixel cross at the center. For a given center c, let s[0..16) be the ring samples in the canonical order:

• SR (sum of “square” responses) compares four opposite quadrants on the ring:

  SR = sum_{k=0..3} | (s[k] + s[k+8]) - (s[k+4] + s[k+12]) |

• DR (sum of “difference” responses) enforces edge‑like structure:

  DR = sum_{k=0..7} | s[k] - s[k+8] |

• μₙ is the mean of all 16 ring samples.

• μₗ is the local mean of the 5‑pixel cross at the center (c, north, south, east, west).

The final ChESS response is:

R = SR - DR - 16 * |μₙ - μₗ|

where high positive values correspond to chessboard‑like corners.

Implementation strategy

Internally the image is processed row‑by‑row, but only pixels whose full ring lies inside the image bounds are evaluated; border pixels are left at zero in the returned ResponseMap.

• Without any features, chess_response_u8 uses a straightforward nested for y { for x { ... } } scalar loop and relies on the compiler’s auto‑vectorization in release builds.
• With the rayon feature, the work is split into independent row slices and processed in parallel using rayon::par_chunks_mut.
• With the simd feature, the inner loop over x is rewritten to operate on LANES pixels at a time using portable SIMD vectors (currently 16 lanes of u8). The ring samples are gathered into SIMD registers, SR/DR/μₙ are accumulated in integer vectors, and the final response is written back per lane.
• With both rayon and simd enabled, each row is processed in parallel and each row uses the SIMD‑accelerated inner loop.

All feature combinations produce the same output values (within a small tolerance for floating‑point rounding), and differ only in performance characteristics.