fast-ssim2 0.8.0

//! # fast-ssim2
//!
//! Fast SIMD-accelerated implementation of [SSIMULACRA2](https://github.com/cloudinary/ssimulacra2),
//! a perceptual image quality metric.
//!
//! ## Quick Start
//!
//! The simplest way to compare two images:
//!
//! ```ignore
//! use fast_ssim2::compute_ssimulacra2;
//! use imgref::ImgVec;
//!
//! // Load your images (8-bit sRGB)
//! let source: ImgVec<[u8; 3]> = load_image("source.png");
//! let distorted: ImgVec<[u8; 3]> = load_image("distorted.png");
//!
//! let score = compute_ssimulacra2(source.as_ref(), distorted.as_ref())?;
//! // score: 100 = identical, 90+ = imperceptible, <50 = significant degradation
//! ```
//!
//! ## Score Interpretation
//!
//! | Score | Quality |
//! |-------|---------|
//! | **100** | Identical (no difference) |
//! | **90+** | Imperceptible difference |
//! | **70-90** | Minor, subtle difference |
//! | **50-70** | Noticeable difference |
//! | **<50** | Significant degradation |
//!
//! ## Supported Input Formats
//!
//! ### With `imgref` feature (recommended for most users)
//!
//! | Type | Color Space | Notes |
//! |------|-------------|-------|
//! | `ImgRef<[u8; 3]>` | sRGB | Standard 8-bit RGB images |
//! | `ImgRef<[u16; 3]>` | sRGB | 16-bit RGB (HDR workflows) |
//! | `ImgRef<[f32; 3]>` | **Linear RGB** | Already linearized data |
//! | `ImgRef<u8>` | sRGB grayscale | Expanded to R=G=B |
//! | `ImgRef<f32>` | Linear grayscale | Expanded to R=G=B |
//!
//! **Convention:** Integer types assume sRGB gamma encoding. Float types assume linear RGB.
//!
//! ### Without features (using `yuvxyb` types)
//!
//! ```
//! use fast_ssim2::compute_ssimulacra2;
//! use yuvxyb::{Rgb, TransferCharacteristic, ColorPrimaries};
//! use std::num::NonZeroUsize;
//!
//! let data: Vec<[f32; 3]> = vec![[0.5, 0.5, 0.5]; 64 * 64];
//! let w = NonZeroUsize::new(64).unwrap();
//! let h = NonZeroUsize::new(64).unwrap();
//! let source = Rgb::new(data.clone(), w, h,
//!     TransferCharacteristic::SRGB, ColorPrimaries::BT709)?;
//! let distorted = Rgb::new(data, w, h,
//!     TransferCharacteristic::SRGB, ColorPrimaries::BT709)?;
//!
//! let score = compute_ssimulacra2(source, distorted)?;
//! // compute_ssimulacra2 accepts yuvxyb::Rgb, yuvxyb::LinearRgb, and more
//! # Ok::<(), Box<dyn std::error::Error>>(())
//! ```
//!
//! ## Batch Comparisons (2x Faster)
//!
//! When comparing multiple images against the same reference (e.g., evaluating
//! different compression levels), precompute the reference data once:
//!
//! ```
//! use fast_ssim2::Ssimulacra2Reference;
//! use yuvxyb::{Rgb, TransferCharacteristic, ColorPrimaries};
//! use std::num::NonZeroUsize;
//!
//! // Create test data
//! let data: Vec<[f32; 3]> = vec![[0.5, 0.5, 0.5]; 64 * 64];
//! let w = NonZeroUsize::new(64).unwrap();
//! let h = NonZeroUsize::new(64).unwrap();
//! let source = Rgb::new(data.clone(), w, h,
//!     TransferCharacteristic::SRGB, ColorPrimaries::BT709)?;
//!
//! // Precompute reference data (~50% of the work)
//! let reference = Ssimulacra2Reference::new(source)?;
//!
//! // Compare multiple distorted versions efficiently
//! let distorted = Rgb::new(data, w, h,
//!     TransferCharacteristic::SRGB, ColorPrimaries::BT709)?;
//! let score = reference.compare(distorted)?;
//! # Ok::<(), Box<dyn std::error::Error>>(())
//! ```
//!
//! ## Custom Input Types
//!
//! Implement [`ToLinearRgb`] to support your own image types:
//!
//! ```
//! use fast_ssim2::{ToLinearRgb, LinearRgbImage, srgb_u8_to_linear};
//!
//! struct MyImage {
//!     pixels: Vec<[u8; 3]>,
//!     width: usize,
//!     height: usize,
//! }
//!
//! impl ToLinearRgb for MyImage {
//!     fn to_linear_rgb(&self) -> LinearRgbImage {
//!         let data: Vec<[f32; 3]> = self.pixels.iter()
//!             .map(|[r, g, b]| [
//!                 srgb_u8_to_linear(*r),
//!                 srgb_u8_to_linear(*g),
//!                 srgb_u8_to_linear(*b),
//!             ])
//!             .collect();
//!         LinearRgbImage::new(data, self.width, self.height)
//!     }
//! }
//! ```
//!
//! Helper functions for sRGB conversion:
//! - [`srgb_u8_to_linear`] - 8-bit lookup table (fastest)
//! - [`srgb_u16_to_linear`] - 16-bit conversion
//! - [`srgb_to_linear`] - General f32 conversion
//!
//! ## SIMD Configuration
//!
//! SIMD is enabled by default via the `archmage` crate, providing cross-platform
//! acceleration on x86_64 (AVX2, AVX-512), AArch64 (NEON), and WASM (SIMD128).
//!
//! | Backend | Speed | Platforms |
//! |---------|-------|-----------|
//! | `Scalar` | 1.0× (baseline) | All |
//! | `Simd` (default) | 2-3× | x86_64, AArch64, WASM |
//!
//! To explicitly select a backend:
//!
//! ```
//! use fast_ssim2::{compute_ssimulacra2_with_config, Ssimulacra2Config};
//!
//! # let source = fast_ssim2::LinearRgbImage::new(vec![[0.0; 3]; 64], 8, 8);
//! # let distorted = fast_ssim2::LinearRgbImage::new(vec![[0.0; 3]; 64], 8, 8);
//! let score = compute_ssimulacra2_with_config(
//!     source,
//!     distorted,
//!     Ssimulacra2Config::scalar(), // or ::simd()
//! )?;
//! # Ok::<(), fast_ssim2::Ssimulacra2Error>(())
//! ```
//!
//! ## Features
//!
//! | Feature | Default | Description |
//! |---------|---------|-------------|
//! | `imgref` | | Support for `imgref` image types |
//! | `rayon` | | Parallel computation |
//!
//! ## Requirements
//!
//! - **Minimum image size:** 8×8 pixels
//! - **MSRV:** 1.89.0

#![forbid(unsafe_code)]

mod blur;
mod input;
mod precompute;
// Reference data for parity testing (hidden from docs but accessible for tests)
#[doc(hidden)]
pub mod reference_data;
#[allow(clippy::too_many_arguments)] // arcane macro generates dispatchers inheriting param count
mod simd_ops;
mod xyb_simd;

pub use blur::Blur;
pub use input::{LinearRgbImage, ToLinearRgb};
pub use precompute::Ssimulacra2Reference;

// Re-export sRGB conversion functions for users implementing custom input types
pub use input::{srgb_to_linear, srgb_u8_to_linear, srgb_u16_to_linear};

// Internal imports for yuvxyb types
use yuvxyb::LinearRgb;
use yuvxyb::Xyb;

// How often to downscale and score the input images.
// Each scaling step will downscale by a factor of two.
pub(crate) const NUM_SCALES: usize = 6;

/// SIMD implementation backend for all operations (blur, XYB conversion, SSIM computation).
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum SimdImpl {
    /// Scalar implementation (baseline, most portable)
    Scalar,
    /// Cross-platform SIMD via archmage (default, AVX2/AVX-512/NEON/WASM128)
    #[default]
    Simd,
}

impl SimdImpl {
    /// Returns the name of this implementation
    pub fn name(&self) -> &'static str {
        match self {
            SimdImpl::Scalar => "scalar",
            SimdImpl::Simd => "simd (archmage)",
        }
    }
}

/// Configuration for SSIMULACRA2 computation.
#[derive(Debug, Clone, Copy, Default)]
pub struct Ssimulacra2Config {
    /// Implementation backend for all operations
    pub impl_type: SimdImpl,
}

impl Ssimulacra2Config {
    /// Create configuration with specified implementation
    pub fn new(impl_type: SimdImpl) -> Self {
        Self { impl_type }
    }

    /// Default configuration using SIMD for all operations
    pub fn simd() -> Self {
        Self::new(SimdImpl::Simd)
    }

    /// Scalar configuration (baseline, most compatible)
    pub fn scalar() -> Self {
        Self::new(SimdImpl::Scalar)
    }
}

/// Errors which can occur when attempting to calculate a SSIMULACRA2 score from two input images.
#[derive(Clone, Copy, Debug, PartialEq, Eq, thiserror::Error)]
pub enum Ssimulacra2Error {
    /// The conversion from input image to [`yuvxyb::LinearRgb`] (via [TryFrom]) returned an [Err].
    #[error("Failed to convert input image to linear RGB")]
    LinearRgbConversionFailed,

    /// The two input images do not have the same width and height.
    #[error("Source and distorted image width and height must be equal")]
    NonMatchingImageDimensions,

    /// One of the input images has a width and/or height of less than 8 pixels.
    #[error("Images must be at least 8x8 pixels")]
    InvalidImageSize,

    /// Gaussian blur operation failed.
    #[error("Gaussian blur operation failed")]
    GaussianBlurError,
}

/// Computes the SSIMULACRA2 score with default configuration (safe SIMD).
#[deprecated(
    since = "0.8.0",
    note = "use compute_ssimulacra2 with ToLinearRgb types instead"
)]
pub fn compute_frame_ssimulacra2<T, U>(source: T, distorted: U) -> Result<f64, Ssimulacra2Error>
where
    LinearRgb: TryFrom<T> + TryFrom<U>,
{
    compute_frame_ssimulacra2_impl(source, distorted, Ssimulacra2Config::default())
}

/// Computes the SSIMULACRA2 score with custom implementation configuration.
#[deprecated(
    since = "0.8.0",
    note = "use compute_ssimulacra2_with_config with ToLinearRgb types instead"
)]
pub fn compute_frame_ssimulacra2_with_config<T, U>(
    source: T,
    distorted: U,
    config: Ssimulacra2Config,
) -> Result<f64, Ssimulacra2Error>
where
    LinearRgb: TryFrom<T> + TryFrom<U>,
{
    compute_frame_ssimulacra2_impl(source, distorted, config)
}

/// Computes the SSIMULACRA2 score from any input type implementing [`ToLinearRgb`].
///
/// This is the recommended API for new code. It supports:
/// - `imgref` types (with the `imgref` feature): `ImgRef<[u8; 3]>`, `ImgRef<[f32; 3]>`, etc.
/// - `yuvxyb` types: `Rgb`, `LinearRgb`
/// - Custom types implementing [`ToLinearRgb`]
///
/// # Color space conventions
/// - Integer types (`u8`, `u16`) are assumed to be sRGB (gamma-encoded)
/// - Float types (`f32`) are assumed to be linear RGB
/// - Grayscale types are expanded to RGB (R=G=B)
///
/// # Example
/// ```ignore
/// use imgref::ImgVec;
/// use fast_ssim2::compute_ssimulacra2;
///
/// let source: ImgVec<[u8; 3]> = /* ... */;
/// let distorted: ImgVec<[u8; 3]> = /* ... */;
/// let score = compute_ssimulacra2(&source, &distorted)?;
/// ```
pub fn compute_ssimulacra2<S, D>(source: S, distorted: D) -> Result<f64, Ssimulacra2Error>
where
    S: ToLinearRgb,
    D: ToLinearRgb,
{
    compute_ssimulacra2_with_config(source, distorted, Ssimulacra2Config::default())
}

/// Computes the SSIMULACRA2 score with custom configuration from [`ToLinearRgb`] inputs.
pub fn compute_ssimulacra2_with_config<S, D>(
    source: S,
    distorted: D,
    config: Ssimulacra2Config,
) -> Result<f64, Ssimulacra2Error>
where
    S: ToLinearRgb,
    D: ToLinearRgb,
{
    let img1: LinearRgb = source.into_linear_rgb().into();
    let img2: LinearRgb = distorted.into_linear_rgb().into();
    compute_frame_ssimulacra2_impl(img1, img2, config)
}

fn compute_frame_ssimulacra2_impl<T, U>(
    source: T,
    distorted: U,
    config: Ssimulacra2Config,
) -> Result<f64, Ssimulacra2Error>
where
    LinearRgb: TryFrom<T> + TryFrom<U>,
{
    let Ok(mut img1) = LinearRgb::try_from(source) else {
        return Err(Ssimulacra2Error::LinearRgbConversionFailed);
    };

    let Ok(mut img2) = LinearRgb::try_from(distorted) else {
        return Err(Ssimulacra2Error::LinearRgbConversionFailed);
    };

    if img1.width() != img2.width() || img1.height() != img2.height() {
        return Err(Ssimulacra2Error::NonMatchingImageDimensions);
    }

    if img1.width().get() < 8 || img1.height().get() < 8 {
        return Err(Ssimulacra2Error::InvalidImageSize);
    }

    let mut width = img1.width().get();
    let mut height = img1.height().get();
    let impl_type = config.impl_type;

    // Pre-allocate reusable buffers (sized for initial dimensions, shrunk per scale)
    let alloc_plane = || vec![0.0f32; width * height];
    let alloc_3planes = || [alloc_plane(), alloc_plane(), alloc_plane()];

    let mut mul = alloc_3planes();
    let mut sigma1_sq = alloc_3planes();
    let mut sigma2_sq = alloc_3planes();
    let mut sigma12 = alloc_3planes();
    let mut mu1 = alloc_3planes();
    let mut mu2 = alloc_3planes();
    let mut img1_planar = alloc_3planes();
    let mut img2_planar = alloc_3planes();

    let mut blur = Blur::with_simd_impl(width, height, impl_type);
    let mut msssim = Msssim::default();

    for scale in 0..NUM_SCALES {
        if width < 8 || height < 8 {
            break;
        }

        if scale > 0 {
            img1 = downscale_by_2(&img1);
            img2 = downscale_by_2(&img2);
            width = img1.width().get();
            height = img2.height().get();
        }

        // Shrink all buffers to current scale size
        let size = width * height;
        for buf in [
            &mut mul,
            &mut sigma1_sq,
            &mut sigma2_sq,
            &mut sigma12,
            &mut mu1,
            &mut mu2,
            &mut img1_planar,
            &mut img2_planar,
        ] {
            for c in buf.iter_mut() {
                c.truncate(size);
            }
        }
        blur.shrink_to(width, height);

        let mut img1_xyb = linear_rgb_to_xyb(img1.clone(), impl_type);
        let mut img2_xyb = linear_rgb_to_xyb(img2.clone(), impl_type);

        make_positive_xyb(&mut img1_xyb);
        make_positive_xyb(&mut img2_xyb);

        xyb_to_planar_into(&img1_xyb, &mut img1_planar);
        xyb_to_planar_into(&img2_xyb, &mut img2_planar);

        image_multiply(&img1_planar, &img1_planar, &mut mul, impl_type);
        blur.blur_into(&mul, &mut sigma1_sq);

        image_multiply(&img2_planar, &img2_planar, &mut mul, impl_type);
        blur.blur_into(&mul, &mut sigma2_sq);

        image_multiply(&img1_planar, &img2_planar, &mut mul, impl_type);
        blur.blur_into(&mul, &mut sigma12);

        blur.blur_into(&img1_planar, &mut mu1);
        blur.blur_into(&img2_planar, &mut mu2);

        let avg_ssim = ssim_map(
            width, height, &mu1, &mu2, &sigma1_sq, &sigma2_sq, &sigma12, impl_type,
        );
        let avg_edgediff = edge_diff_map(
            width,
            height,
            &img1_planar,
            &mu1,
            &img2_planar,
            &mu2,
            impl_type,
        );
        msssim.scales.push(MsssimScale {
            avg_ssim,
            avg_edgediff,
        });
    }

    Ok(msssim.score())
}

/// Convert LinearRgb to Xyb using the specified implementation
fn linear_rgb_to_xyb(linear_rgb: LinearRgb, impl_type: SimdImpl) -> Xyb {
    match impl_type {
        SimdImpl::Scalar => Xyb::from(linear_rgb),
        SimdImpl::Simd => {
            let width = linear_rgb.width(); // NonZeroUsize
            let height = linear_rgb.height(); // NonZeroUsize
            let mut data = linear_rgb.into_data();
            xyb_simd::linear_rgb_to_xyb_simd(&mut data);
            Xyb::new(data, width, height).expect("XYB construction should not fail")
        }
    }
}

// For backwards compatibility
pub(crate) fn linear_rgb_to_xyb_simd(linear_rgb: LinearRgb) -> Xyb {
    linear_rgb_to_xyb(linear_rgb, SimdImpl::Simd)
}

pub(crate) fn make_positive_xyb(xyb: &mut Xyb) {
    for pix in xyb.data_mut().iter_mut() {
        pix[2] = (pix[2] - pix[1]) + 0.55;
        pix[0] = (pix[0]).mul_add(14.0, 0.42);
        pix[1] += 0.01;
    }
}

pub(crate) fn xyb_to_planar(xyb: &Xyb) -> [Vec<f32>; 3] {
    let size = xyb.width().get() * xyb.height().get();
    let mut out = [vec![0.0f32; size], vec![0.0f32; size], vec![0.0f32; size]];
    xyb_to_planar_into(xyb, &mut out);
    out
}

/// Convert XYB to planar format into pre-allocated buffers (zero-allocation)
pub(crate) fn xyb_to_planar_into(xyb: &Xyb, out: &mut [Vec<f32>; 3]) {
    let [out0, out1, out2] = out;
    for (((i, o0), o1), o2) in xyb
        .data()
        .iter()
        .copied()
        .zip(out0.iter_mut())
        .zip(out1.iter_mut())
        .zip(out2.iter_mut())
    {
        *o0 = i[0];
        *o1 = i[1];
        *o2 = i[2];
    }
}

pub(crate) fn image_multiply(
    img1: &[Vec<f32>; 3],
    img2: &[Vec<f32>; 3],
    out: &mut [Vec<f32>; 3],
    impl_type: SimdImpl,
) {
    match impl_type {
        SimdImpl::Scalar => image_multiply_scalar(img1, img2, out),
        SimdImpl::Simd => simd_ops::image_multiply_simd(img1, img2, out),
    }
}

fn image_multiply_scalar(img1: &[Vec<f32>; 3], img2: &[Vec<f32>; 3], out: &mut [Vec<f32>; 3]) {
    for ((plane1, plane2), out_plane) in img1.iter().zip(img2.iter()).zip(out.iter_mut()) {
        for ((&p1, &p2), o) in plane1.iter().zip(plane2.iter()).zip(out_plane.iter_mut()) {
            *o = p1 * p2;
        }
    }
}

pub(crate) fn downscale_by_2(in_data: &LinearRgb) -> LinearRgb {
    use std::num::NonZeroUsize;
    const SCALE: usize = 2;
    let in_w = in_data.width().get();
    let in_h = in_data.height().get();
    let out_w = in_w.div_ceil(SCALE);
    let out_h = in_h.div_ceil(SCALE);
    let mut out_data = vec![[0.0f32; 3]; out_w * out_h];
    let normalize = 1.0f32 / (SCALE * SCALE) as f32;

    let in_data = &in_data.data();
    for oy in 0..out_h {
        for ox in 0..out_w {
            for c in 0..3 {
                let mut sum = 0f32;
                for iy in 0..SCALE {
                    for ix in 0..SCALE {
                        let x = (ox * SCALE + ix).min(in_w - 1);
                        let y = (oy * SCALE + iy).min(in_h - 1);
                        sum += in_data[y * in_w + x][c];
                    }
                }
                out_data[oy * out_w + ox][c] = sum * normalize;
            }
        }
    }

    LinearRgb::new(
        out_data,
        NonZeroUsize::new(out_w).expect("out_w must be nonzero"),
        NonZeroUsize::new(out_h).expect("out_h must be nonzero"),
    )
    .expect("Resolution and data size match")
}

#[allow(clippy::too_many_arguments)]
pub(crate) fn ssim_map(
    width: usize,
    height: usize,
    m1: &[Vec<f32>; 3],
    m2: &[Vec<f32>; 3],
    s11: &[Vec<f32>; 3],
    s22: &[Vec<f32>; 3],
    s12: &[Vec<f32>; 3],
    impl_type: SimdImpl,
) -> [f64; 3 * 2] {
    match impl_type {
        SimdImpl::Scalar => ssim_map_scalar(width, height, m1, m2, s11, s22, s12),
        SimdImpl::Simd => simd_ops::ssim_map_simd(width, height, m1, m2, s11, s22, s12),
    }
}

fn ssim_map_scalar(
    width: usize,
    height: usize,
    m1: &[Vec<f32>; 3],
    m2: &[Vec<f32>; 3],
    s11: &[Vec<f32>; 3],
    s22: &[Vec<f32>; 3],
    s12: &[Vec<f32>; 3],
) -> [f64; 3 * 2] {
    const C2: f32 = 0.0009f32;

    let one_per_pixels = 1.0f64 / (width * height) as f64;
    let mut plane_averages = [0f64; 3 * 2];

    for c in 0..3 {
        let mut sum_d = 0.0f64;
        let mut sum_d4 = 0.0f64;
        for (row_m1, (row_m2, (row_s11, (row_s22, row_s12)))) in m1[c].chunks_exact(width).zip(
            m2[c].chunks_exact(width).zip(
                s11[c]
                    .chunks_exact(width)
                    .zip(s22[c].chunks_exact(width).zip(s12[c].chunks_exact(width))),
            ),
        ) {
            for x in 0..width {
                let mu1 = row_m1[x];
                let mu2 = row_m2[x];
                let mu11 = mu1 * mu1;
                let mu22 = mu2 * mu2;
                let mu12 = mu1 * mu2;
                let mu_diff = mu1 - mu2;

                let num_m = mu_diff.mul_add(-mu_diff, 1.0f32);
                let num_s = 2.0f32.mul_add(row_s12[x] - mu12, C2);
                let denom_s = (row_s11[x] - mu11) + (row_s22[x] - mu22) + C2;
                let d = (1.0f32 - (num_m * num_s) / denom_s).max(0.0f32);
                let d2 = d * d;
                let d4 = d2 * d2;
                sum_d += f64::from(d);
                sum_d4 += f64::from(d4);
            }
        }
        plane_averages[c * 2] = one_per_pixels * sum_d;
        plane_averages[c * 2 + 1] = (one_per_pixels * sum_d4).sqrt().sqrt();
    }

    plane_averages
}

pub(crate) fn edge_diff_map(
    width: usize,
    height: usize,
    img1: &[Vec<f32>; 3],
    mu1: &[Vec<f32>; 3],
    img2: &[Vec<f32>; 3],
    mu2: &[Vec<f32>; 3],
    impl_type: SimdImpl,
) -> [f64; 3 * 4] {
    match impl_type {
        SimdImpl::Scalar => edge_diff_map_scalar(width, height, img1, mu1, img2, mu2),
        SimdImpl::Simd => simd_ops::edge_diff_map_simd(width, height, img1, mu1, img2, mu2),
    }
}

fn edge_diff_map_scalar(
    width: usize,
    height: usize,
    img1: &[Vec<f32>; 3],
    mu1: &[Vec<f32>; 3],
    img2: &[Vec<f32>; 3],
    mu2: &[Vec<f32>; 3],
) -> [f64; 3 * 4] {
    let one_per_pixels = 1.0f64 / (width * height) as f64;
    let mut plane_averages = [0f64; 3 * 4];

    for c in 0..3 {
        let mut sum1 = [0.0f64; 4];
        for (row1, (row2, (rowm1, rowm2))) in img1[c].chunks_exact(width).zip(
            img2[c]
                .chunks_exact(width)
                .zip(mu1[c].chunks_exact(width).zip(mu2[c].chunks_exact(width))),
        ) {
            for x in 0..width {
                let d1: f64 = (1.0 + f64::from((row2[x] - rowm2[x]).abs()))
                    / (1.0 + f64::from((row1[x] - rowm1[x]).abs()))
                    - 1.0;

                let artifact = d1.max(0.0);
                sum1[0] += artifact;
                sum1[1] += artifact.powi(4);

                let detail_lost = (-d1).max(0.0);
                sum1[2] += detail_lost;
                sum1[3] += detail_lost.powi(4);
            }
        }
        plane_averages[c * 4] = one_per_pixels * sum1[0];
        plane_averages[c * 4 + 1] = (one_per_pixels * sum1[1]).sqrt().sqrt();
        plane_averages[c * 4 + 2] = one_per_pixels * sum1[2];
        plane_averages[c * 4 + 3] = (one_per_pixels * sum1[3]).sqrt().sqrt();
    }

    plane_averages
}

#[derive(Debug, Clone, Default)]
pub(crate) struct Msssim {
    pub scales: Vec<MsssimScale>,
}

#[derive(Debug, Clone, Copy, Default)]
pub(crate) struct MsssimScale {
    pub avg_ssim: [f64; 3 * 2],
    pub avg_edgediff: [f64; 3 * 4],
}

impl Msssim {
    #[allow(clippy::too_many_lines)]
    pub fn score(&self) -> f64 {
        const WEIGHT: [f64; 108] = [
            0.0,
            0.000_737_660_670_740_658_6,
            0.0,
            0.0,
            0.000_779_348_168_286_730_9,
            0.0,
            0.0,
            0.000_437_115_573_010_737_9,
            0.0,
            1.104_172_642_665_734_6,
            0.000_662_848_341_292_71,
            0.000_152_316_327_837_187_52,
            0.0,
            0.001_640_643_745_659_975_4,
            0.0,
            1.842_245_552_053_929_8,
            11.441_172_603_757_666,
            0.0,
            0.000_798_910_943_601_516_3,
            0.000_176_816_438_078_653,
            0.0,
            1.878_759_497_954_638_7,
            10.949_069_906_051_42,
            0.0,
            0.000_728_934_699_150_807_2,
            0.967_793_708_062_683_3,
            0.0,
            0.000_140_034_242_854_358_84,
            0.998_176_697_785_496_7,
            0.000_319_497_559_344_350_53,
            0.000_455_099_211_379_206_3,
            0.0,
            0.0,
            0.001_364_876_616_324_339_8,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            7.466_890_328_078_848,
            0.0,
            17.445_833_984_131_262,
            0.000_623_560_163_404_146_6,
            0.0,
            0.0,
            6.683_678_146_179_332,
            0.000_377_244_079_796_112_96,
            1.027_889_937_768_264,
            225.205_153_008_492_74,
            0.0,
            0.0,
            19.213_238_186_143_016,
            0.001_140_152_458_661_836_1,
            0.001_237_755_635_509_985,
            176.393_175_984_506_94,
            0.0,
            0.0,
            24.433_009_998_704_76,
            0.285_208_026_121_177_57,
            0.000_448_543_692_383_340_8,
            0.0,
            0.0,
            0.0,
            34.779_063_444_837_72,
            44.835_625_328_877_896,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            0.000_868_055_657_329_169_8,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            0.000_531_319_187_435_874_7,
            0.0,
            0.000_165_338_141_613_791_12,
            0.0,
            0.0,
            0.0,
            0.0,
            0.0,
            0.000_417_917_180_325_133_6,
            0.001_729_082_823_472_283_3,
            0.0,
            0.002_082_700_584_663_643_7,
            0.0,
            0.0,
            8.826_982_764_996_862,
            23.192_433_439_989_26,
            0.0,
            95.108_049_881_108_6,
            0.986_397_803_440_068_2,
            0.983_438_279_246_535_3,
            0.001_228_640_504_827_849_3,
            171.266_725_589_730_7,
            0.980_785_887_243_537_9,
            0.0,
            0.0,
            0.0,
            0.000_513_006_458_899_067_9,
            0.0,
            0.000_108_540_578_584_115_37,
        ];

        let mut ssim = 0.0f64;

        let mut i = 0usize;
        for c in 0..3 {
            for scale in &self.scales {
                for n in 0..2 {
                    ssim = WEIGHT[i].mul_add(scale.avg_ssim[c * 2 + n].abs(), ssim);
                    i += 1;
                    ssim = WEIGHT[i].mul_add(scale.avg_edgediff[c * 4 + n].abs(), ssim);
                    i += 1;
                    ssim = WEIGHT[i].mul_add(scale.avg_edgediff[c * 4 + n + 2].abs(), ssim);
                    i += 1;
                }
            }
        }

        ssim *= 0.956_238_261_683_484_4_f64;
        ssim = (6.248_496_625_763_138e-5 * ssim * ssim).mul_add(
            ssim,
            2.326_765_642_916_932f64.mul_add(ssim, -0.020_884_521_182_843_837 * ssim * ssim),
        );

        if ssim > 0.0f64 {
            ssim = ssim
                .powf(0.627_633_646_783_138_7)
                .mul_add(-10.0f64, 100.0f64);
        } else {
            ssim = 100.0f64;
        }

        ssim
    }
}

#[cfg(test)]
#[allow(deprecated)]
mod tests {
    use std::path::PathBuf;

    use super::*;
    use yuvxyb::{ColorPrimaries, Rgb, TransferCharacteristic};

    #[test]
    fn test_ssimulacra2() {
        let source = image::open(
            PathBuf::from(env!("CARGO_MANIFEST_DIR"))
                .join("test_data")
                .join("tank_source.png"),
        )
        .unwrap();
        let distorted = image::open(
            PathBuf::from(env!("CARGO_MANIFEST_DIR"))
                .join("test_data")
                .join("tank_distorted.png"),
        )
        .unwrap();
        let source_data = source
            .to_rgb32f()
            .chunks_exact(3)
            .map(|chunk| [chunk[0], chunk[1], chunk[2]])
            .collect::<Vec<_>>();
        let source_data = Xyb::try_from(
            Rgb::new(
                source_data,
                std::num::NonZeroUsize::new(source.width() as usize).unwrap(),
                std::num::NonZeroUsize::new(source.height() as usize).unwrap(),
                TransferCharacteristic::SRGB,
                ColorPrimaries::BT709,
            )
            .unwrap(),
        )
        .unwrap();
        let distorted_data = distorted
            .to_rgb32f()
            .chunks_exact(3)
            .map(|chunk| [chunk[0], chunk[1], chunk[2]])
            .collect::<Vec<_>>();
        let distorted_data = Xyb::try_from(
            Rgb::new(
                distorted_data,
                std::num::NonZeroUsize::new(distorted.width() as usize).unwrap(),
                std::num::NonZeroUsize::new(distorted.height() as usize).unwrap(),
                TransferCharacteristic::SRGB,
                ColorPrimaries::BT709,
            )
            .unwrap(),
        )
        .unwrap();
        let result = compute_frame_ssimulacra2(source_data, distorted_data).unwrap();
        let expected = 17.398_505_f64;
        assert!(
            (result - expected).abs() < 0.25f64,
            "Result {result:.6} not equal to expected {expected:.6}",
        );
    }

    #[test]
    fn test_xyb_simd_vs_yuvxyb() {
        use yuvxyb::{ColorPrimaries, TransferCharacteristic};

        let source = image::open(
            PathBuf::from(env!("CARGO_MANIFEST_DIR"))
                .join("test_data")
                .join("tank_source.png"),
        )
        .unwrap();

        let source_data: Vec<[f32; 3]> = source
            .to_rgb32f()
            .chunks_exact(3)
            .map(|chunk| [chunk[0], chunk[1], chunk[2]])
            .collect();

        let width = source.width() as usize;
        let height = source.height() as usize;
        let nz_width = std::num::NonZeroUsize::new(width).unwrap();
        let nz_height = std::num::NonZeroUsize::new(height).unwrap();

        let rgb_for_yuvxyb = Rgb::new(
            source_data.clone(),
            nz_width,
            nz_height,
            TransferCharacteristic::SRGB,
            ColorPrimaries::BT709,
        )
        .unwrap();
        let lrgb_for_yuvxyb = yuvxyb::LinearRgb::try_from(rgb_for_yuvxyb).unwrap();
        let xyb_yuvxyb = yuvxyb::Xyb::from(lrgb_for_yuvxyb);

        let rgb_for_simd = Rgb::new(
            source_data,
            nz_width,
            nz_height,
            TransferCharacteristic::SRGB,
            ColorPrimaries::BT709,
        )
        .unwrap();
        let lrgb_for_simd = LinearRgb::try_from(rgb_for_simd).unwrap();
        let xyb_simd = linear_rgb_to_xyb_simd(lrgb_for_simd);

        let mut max_diff = [0.0f32; 3];
        for (yuvxyb_pix, simd_pix) in xyb_yuvxyb.data().iter().zip(xyb_simd.data().iter()) {
            for c in 0..3 {
                let diff = (yuvxyb_pix[c] - simd_pix[c]).abs();
                max_diff[c] = max_diff[c].max(diff);
            }
        }

        assert!(
            max_diff[0] < 1e-5 && max_diff[1] < 1e-5 && max_diff[2] < 1e-5,
            "SIMD XYB differs from yuvxyb: max_diff={:?}",
            max_diff
        );
    }
}