png/filter.rs
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use core::convert::TryInto;
use crate::common::BytesPerPixel;
/// SIMD helpers for `fn unfilter`
///
/// TODO(https://github.com/rust-lang/rust/issues/86656): Stop gating this module behind the
/// "unstable" feature of the `png` crate. This should be possible once the "portable_simd"
/// feature of Rust gets stabilized.
#[cfg(feature = "unstable")]
mod simd {
use std::simd::cmp::{SimdOrd, SimdPartialEq, SimdPartialOrd};
use std::simd::num::{SimdInt, SimdUint};
use std::simd::{u8x4, u8x8, LaneCount, Simd, SimdElement, SupportedLaneCount};
/// This is an equivalent of the `PaethPredictor` function from
/// [the spec](http://www.libpng.org/pub/png/spec/1.2/PNG-Filters.html#Filter-type-4-Paeth)
/// except that it simultaneously calculates the predictor for all SIMD lanes.
/// Mapping between parameter names and pixel positions can be found in
/// [a diagram here](https://www.w3.org/TR/png/#filter-byte-positions).
///
/// Examples of how different pixel types may be represented as multiple SIMD lanes:
/// - RGBA => 4 lanes of `i16x4` contain R, G, B, A
/// - RGB => 4 lanes of `i16x4` contain R, G, B, and a ignored 4th value
///
/// The SIMD algorithm below is based on [`libpng`](https://github.com/glennrp/libpng/blob/f8e5fa92b0e37ab597616f554bee254157998227/intel/filter_sse2_intrinsics.c#L261-L280).
fn paeth_predictor<const N: usize>(
a: Simd<i16, N>,
b: Simd<i16, N>,
c: Simd<i16, N>,
) -> Simd<i16, N>
where
LaneCount<N>: SupportedLaneCount,
{
let pa = b - c; // (p-a) == (a+b-c - a) == (b-c)
let pb = a - c; // (p-b) == (a+b-c - b) == (a-c)
let pc = pa + pb; // (p-c) == (a+b-c - c) == (a+b-c-c) == (b-c)+(a-c)
let pa = pa.abs();
let pb = pb.abs();
let pc = pc.abs();
let smallest = pc.simd_min(pa.simd_min(pb));
// Paeth algorithm breaks ties favoring a over b over c, so we execute the following
// lane-wise selection:
//
// if smalest == pa
// then select a
// else select (if smallest == pb then select b else select c)
smallest
.simd_eq(pa)
.select(a, smallest.simd_eq(pb).select(b, c))
}
/// Equivalent to `simd::paeth_predictor` but does not temporarily convert
/// the SIMD elements to `i16`.
fn paeth_predictor_u8<const N: usize>(
a: Simd<u8, N>,
b: Simd<u8, N>,
c: Simd<u8, N>,
) -> Simd<u8, N>
where
LaneCount<N>: SupportedLaneCount,
{
// Calculates the absolute difference between `a` and `b`.
fn abs_diff_simd<const N: usize>(a: Simd<u8, N>, b: Simd<u8, N>) -> Simd<u8, N>
where
LaneCount<N>: SupportedLaneCount,
{
a.simd_max(b) - b.simd_min(a)
}
// Uses logic from `filter::filter_paeth` to calculate absolute values
// entirely in `Simd<u8, N>`. This method avoids unpacking and packing
// penalties resulting from conversion to and from `Simd<i16, N>`.
// ```
// let pa = b.max(c) - c.min(b);
// let pb = a.max(c) - c.min(a);
// let pc = if (a < c) == (c < b) {
// pa.max(pb) - pa.min(pb)
// } else {
// 255
// };
// ```
let pa = abs_diff_simd(b, c);
let pb = abs_diff_simd(a, c);
let pc = a
.simd_lt(c)
.simd_eq(c.simd_lt(b))
.select(abs_diff_simd(pa, pb), Simd::splat(255));
let smallest = pc.simd_min(pa.simd_min(pb));
// Paeth algorithm breaks ties favoring a over b over c, so we execute the following
// lane-wise selection:
//
// if smalest == pa
// then select a
// else select (if smallest == pb then select b else select c)
smallest
.simd_eq(pa)
.select(a, smallest.simd_eq(pb).select(b, c))
}
/// Memory of previous pixels (as needed to unfilter `FilterType::Paeth`).
/// See also https://www.w3.org/TR/png/#filter-byte-positions
#[derive(Default)]
struct PaethState<T, const N: usize>
where
T: SimdElement,
LaneCount<N>: SupportedLaneCount,
{
/// Previous pixel in the previous row.
c: Simd<T, N>,
/// Previous pixel in the current row.
a: Simd<T, N>,
}
/// Mutates `x` as needed to unfilter `FilterType::Paeth`.
///
/// `b` is the current pixel in the previous row. `x` is the current pixel in the current row.
/// See also https://www.w3.org/TR/png/#filter-byte-positions
fn paeth_step<const N: usize>(
state: &mut PaethState<i16, N>,
b: Simd<u8, N>,
x: &mut Simd<u8, N>,
) where
LaneCount<N>: SupportedLaneCount,
{
// Storing the inputs.
let b = b.cast::<i16>();
// Calculating the new value of the current pixel.
let predictor = paeth_predictor(state.a, b, state.c);
*x += predictor.cast::<u8>();
// Preparing for the next step.
state.c = b;
state.a = x.cast::<i16>();
}
/// Computes the Paeth predictor without converting `u8` to `i16`.
///
/// See `simd::paeth_step`.
fn paeth_step_u8<const N: usize>(
state: &mut PaethState<u8, N>,
b: Simd<u8, N>,
x: &mut Simd<u8, N>,
) where
LaneCount<N>: SupportedLaneCount,
{
// Calculating the new value of the current pixel.
*x += paeth_predictor_u8(state.a, b, state.c);
// Preparing for the next step.
state.c = b;
state.a = *x;
}
fn load3(src: &[u8]) -> u8x4 {
u8x4::from_array([src[0], src[1], src[2], 0])
}
fn store3(src: u8x4, dest: &mut [u8]) {
dest[0..3].copy_from_slice(&src.to_array()[0..3])
}
/// Undoes `FilterType::Paeth` for `BytesPerPixel::Three`.
pub fn unfilter_paeth3(mut prev_row: &[u8], mut curr_row: &mut [u8]) {
debug_assert_eq!(prev_row.len(), curr_row.len());
debug_assert_eq!(prev_row.len() % 3, 0);
let mut state = PaethState::<i16, 4>::default();
while prev_row.len() >= 4 {
// `u8x4` requires working with `[u8;4]`, but we can just load and ignore the first
// byte from the next triple. This optimization technique mimics the algorithm found
// in
// https://github.com/glennrp/libpng/blob/f8e5fa92b0e37ab597616f554bee254157998227/intel/filter_sse2_intrinsics.c#L130-L131
let b = u8x4::from_slice(prev_row);
let mut x = u8x4::from_slice(curr_row);
paeth_step(&mut state, b, &mut x);
// We can speculate that writing 4 bytes might be more efficient (just as with using
// `u8x4::from_slice` above), but we can't use that here, because we can't clobber the
// first byte of the next pixel in the `curr_row`.
store3(x, curr_row);
prev_row = &prev_row[3..];
curr_row = &mut curr_row[3..];
}
// Can't use `u8x4::from_slice` for the last `[u8;3]`.
let b = load3(prev_row);
let mut x = load3(curr_row);
paeth_step(&mut state, b, &mut x);
store3(x, curr_row);
}
/// Undoes `FilterType::Paeth` for `BytesPerPixel::Four` and `BytesPerPixel::Eight`.
///
/// This function calculates the Paeth predictor entirely in `Simd<u8, N>`
/// without converting to an intermediate `Simd<i16, N>`. Doing so avoids
/// paying a small performance penalty converting between types.
pub fn unfilter_paeth_u8<const N: usize>(prev_row: &[u8], curr_row: &mut [u8])
where
LaneCount<N>: SupportedLaneCount,
{
debug_assert_eq!(prev_row.len(), curr_row.len());
debug_assert_eq!(prev_row.len() % N, 0);
assert!(matches!(N, 4 | 8));
let mut state = PaethState::<u8, N>::default();
for (prev_row, curr_row) in prev_row.chunks_exact(N).zip(curr_row.chunks_exact_mut(N)) {
let b = Simd::from_slice(prev_row);
let mut x = Simd::from_slice(curr_row);
paeth_step_u8(&mut state, b, &mut x);
curr_row[..N].copy_from_slice(&x.to_array()[..N]);
}
}
fn load6(src: &[u8]) -> u8x8 {
u8x8::from_array([src[0], src[1], src[2], src[3], src[4], src[5], 0, 0])
}
fn store6(src: u8x8, dest: &mut [u8]) {
dest[0..6].copy_from_slice(&src.to_array()[0..6])
}
/// Undoes `FilterType::Paeth` for `BytesPerPixel::Six`.
pub fn unfilter_paeth6(mut prev_row: &[u8], mut curr_row: &mut [u8]) {
debug_assert_eq!(prev_row.len(), curr_row.len());
debug_assert_eq!(prev_row.len() % 6, 0);
let mut state = PaethState::<i16, 8>::default();
while prev_row.len() >= 8 {
// `u8x8` requires working with `[u8;8]`, but we can just load and ignore the first two
// bytes from the next pixel. This optimization technique mimics the algorithm found
// in
// https://github.com/glennrp/libpng/blob/f8e5fa92b0e37ab597616f554bee254157998227/intel/filter_sse2_intrinsics.c#L130-L131
let b = u8x8::from_slice(prev_row);
let mut x = u8x8::from_slice(curr_row);
paeth_step(&mut state, b, &mut x);
// We can speculate that writing 8 bytes might be more efficient (just as with using
// `u8x8::from_slice` above), but we can't use that here, because we can't clobber the
// first bytes of the next pixel in the `curr_row`.
store6(x, curr_row);
prev_row = &prev_row[6..];
curr_row = &mut curr_row[6..];
}
// Can't use `u8x8::from_slice` for the last `[u8;6]`.
let b = load6(prev_row);
let mut x = load6(curr_row);
paeth_step(&mut state, b, &mut x);
store6(x, curr_row);
}
}
/// The byte level filter applied to scanlines to prepare them for compression.
///
/// Compression in general benefits from repetitive data. The filter is a content-aware method of
/// compressing the range of occurring byte values to help the compression algorithm. Note that
/// this does not operate on pixels but on raw bytes of a scanline.
///
/// Details on how each filter works can be found in the [PNG Book](http://www.libpng.org/pub/png/book/chapter09.html).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
#[repr(u8)]
pub enum FilterType {
NoFilter = 0,
Sub = 1,
Up = 2,
Avg = 3,
Paeth = 4,
}
impl Default for FilterType {
fn default() -> Self {
FilterType::Sub
}
}
impl FilterType {
/// u8 -> Self. Temporary solution until Rust provides a canonical one.
pub fn from_u8(n: u8) -> Option<FilterType> {
match n {
0 => Some(FilterType::NoFilter),
1 => Some(FilterType::Sub),
2 => Some(FilterType::Up),
3 => Some(FilterType::Avg),
4 => Some(FilterType::Paeth),
_ => None,
}
}
}
/// Adaptive filtering tries every possible filter for each row and uses a heuristic to select the best one.
/// This improves compression ratio, but makes encoding slightly slower.
///
/// It is recommended to use `Adaptive` whenever you care about compression ratio.
/// Filtering is quite cheap compared to other parts of encoding, but can contribute
/// to the compression ratio significantly.
///
/// `NonAdaptive` filtering is the default.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
#[repr(u8)]
pub enum AdaptiveFilterType {
Adaptive,
NonAdaptive,
}
impl Default for AdaptiveFilterType {
fn default() -> Self {
AdaptiveFilterType::NonAdaptive
}
}
fn filter_paeth_decode(a: u8, b: u8, c: u8) -> u8 {
// Decoding seems to optimize better with this algorithm
let pa = (i16::from(b) - i16::from(c)).abs();
let pb = (i16::from(a) - i16::from(c)).abs();
let pc = ((i16::from(a) - i16::from(c)) + (i16::from(b) - i16::from(c))).abs();
let mut out = a;
let mut min = pa;
if pb < min {
min = pb;
out = b;
}
if pc < min {
out = c;
}
out
}
fn filter_paeth(a: u8, b: u8, c: u8) -> u8 {
// This is an optimized version of the paeth filter from the PNG specification, proposed by
// Luca Versari for [FPNGE](https://www.lucaversari.it/FJXL_and_FPNGE.pdf). It operates
// entirely on unsigned 8-bit quantities, making it more conducive to vectorization.
//
// p = a + b - c
// pa = |p - a| = |a + b - c - a| = |b - c| = max(b, c) - min(b, c)
// pb = |p - b| = |a + b - c - b| = |a - c| = max(a, c) - min(a, c)
// pc = |p - c| = |a + b - c - c| = |(b - c) + (a - c)| = ...
//
// Further optimizing the calculation of `pc` a bit tricker. However, notice that:
//
// a > c && b > c
// ==> (a - c) > 0 && (b - c) > 0
// ==> pc > (a - c) && pc > (b - c)
// ==> pc > |a - c| && pc > |b - c|
// ==> pc > pb && pc > pa
//
// Meaning that if `c` is smaller than `a` and `b`, the value of `pc` is irrelevant. Similar
// reasoning applies if `c` is larger than the other two inputs. Assuming that `c >= b` and
// `c <= b` or vice versa:
//
// pc = ||b - c| - |a - c|| = |pa - pb| = max(pa, pb) - min(pa, pb)
//
let pa = b.max(c) - c.min(b);
let pb = a.max(c) - c.min(a);
let pc = if (a < c) == (c < b) {
pa.max(pb) - pa.min(pb)
} else {
255
};
if pa <= pb && pa <= pc {
a
} else if pb <= pc {
b
} else {
c
}
}
pub(crate) fn unfilter(
mut filter: FilterType,
tbpp: BytesPerPixel,
previous: &[u8],
current: &mut [u8],
) {
use self::FilterType::*;
// If the previous row is empty, then treat it as if it were filled with zeros.
if previous.is_empty() {
if filter == Paeth {
filter = Sub;
} else if filter == Up {
filter = NoFilter;
}
}
// [2023/01 @okaneco] - Notes on optimizing decoding filters
//
// Links:
// [PR]: https://github.com/image-rs/image-png/pull/382
// [SWAR]: http://aggregate.org/SWAR/over.html
// [AVG]: http://aggregate.org/MAGIC/#Average%20of%20Integers
//
// #382 heavily refactored and optimized the following filters making the
// implementation nonobvious. These comments function as a summary of that
// PR with an explanation of the choices made below.
//
// #382 originally started with trying to optimize using a technique called
// SWAR, SIMD Within a Register. SWAR uses regular integer types like `u32`
// and `u64` as SIMD registers to perform vertical operations in parallel,
// usually involving bit-twiddling. This allowed each `BytesPerPixel` (bpp)
// pixel to be decoded in parallel: 3bpp and 4bpp in a `u32`, 6bpp and 8pp
// in a `u64`. The `Sub` filter looked like the following code block, `Avg`
// was similar but used a bitwise average method from [AVG]:
// ```
// // See "Unpartitioned Operations With Correction Code" from [SWAR]
// fn swar_add_u32(x: u32, y: u32) -> u32 {
// // 7-bit addition so there's no carry over the most significant bit
// let n = (x & 0x7f7f7f7f) + (y & 0x7f7f7f7f); // 0x7F = 0b_0111_1111
// // 1-bit parity/XOR addition to fill in the missing MSB
// n ^ (x ^ y) & 0x80808080 // 0x80 = 0b_1000_0000
// }
//
// let mut prev =
// u32::from_ne_bytes([current[0], current[1], current[2], current[3]]);
// for chunk in current[4..].chunks_exact_mut(4) {
// let cur = u32::from_ne_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
// let new_chunk = swar_add_u32(cur, prev);
// chunk.copy_from_slice(&new_chunk.to_ne_bytes());
// prev = new_chunk;
// }
// ```
// While this provided a measurable increase, @fintelia found that this idea
// could be taken even further by unrolling the chunks component-wise and
// avoiding unnecessary byte-shuffling by using byte arrays instead of
// `u32::from|to_ne_bytes`. The bitwise operations were no longer necessary
// so they were reverted to their obvious arithmetic equivalent. Lastly,
// `TryInto` was used instead of `copy_from_slice`. The `Sub` code now
// looked like this (with asserts to remove `0..bpp` bounds checks):
// ```
// assert!(len > 3);
// let mut prev = [current[0], current[1], current[2], current[3]];
// for chunk in current[4..].chunks_exact_mut(4) {
// let new_chunk = [
// chunk[0].wrapping_add(prev[0]),
// chunk[1].wrapping_add(prev[1]),
// chunk[2].wrapping_add(prev[2]),
// chunk[3].wrapping_add(prev[3]),
// ];
// *TryInto::<&mut [u8; 4]>::try_into(chunk).unwrap() = new_chunk;
// prev = new_chunk;
// }
// ```
// The compiler was able to optimize the code to be even faster and this
// method even sped up Paeth filtering! Assertions were experimentally
// added within loop bodies which produced better instructions but no
// difference in speed. Finally, the code was refactored to remove manual
// slicing and start the previous pixel chunks with arrays of `[0; N]`.
// ```
// let mut prev = [0; 4];
// for chunk in current.chunks_exact_mut(4) {
// let new_chunk = [
// chunk[0].wrapping_add(prev[0]),
// chunk[1].wrapping_add(prev[1]),
// chunk[2].wrapping_add(prev[2]),
// chunk[3].wrapping_add(prev[3]),
// ];
// *TryInto::<&mut [u8; 4]>::try_into(chunk).unwrap() = new_chunk;
// prev = new_chunk;
// }
// ```
// While we're not manually bit-twiddling anymore, a possible takeaway from
// this is to "think in SWAR" when dealing with small byte arrays. Unrolling
// array operations and performing them component-wise may unlock previously
// unavailable optimizations from the compiler, even when using the
// `chunks_exact` methods for their potential auto-vectorization benefits.
match filter {
NoFilter => {}
Sub => match tbpp {
BytesPerPixel::One => {
current.iter_mut().reduce(|&mut prev, curr| {
*curr = curr.wrapping_add(prev);
curr
});
}
BytesPerPixel::Two => {
let mut prev = [0; 2];
for chunk in current.chunks_exact_mut(2) {
let new_chunk = [
chunk[0].wrapping_add(prev[0]),
chunk[1].wrapping_add(prev[1]),
];
*TryInto::<&mut [u8; 2]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Three => {
let mut prev = [0; 3];
for chunk in current.chunks_exact_mut(3) {
let new_chunk = [
chunk[0].wrapping_add(prev[0]),
chunk[1].wrapping_add(prev[1]),
chunk[2].wrapping_add(prev[2]),
];
*TryInto::<&mut [u8; 3]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Four => {
let mut prev = [0; 4];
for chunk in current.chunks_exact_mut(4) {
let new_chunk = [
chunk[0].wrapping_add(prev[0]),
chunk[1].wrapping_add(prev[1]),
chunk[2].wrapping_add(prev[2]),
chunk[3].wrapping_add(prev[3]),
];
*TryInto::<&mut [u8; 4]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Six => {
let mut prev = [0; 6];
for chunk in current.chunks_exact_mut(6) {
let new_chunk = [
chunk[0].wrapping_add(prev[0]),
chunk[1].wrapping_add(prev[1]),
chunk[2].wrapping_add(prev[2]),
chunk[3].wrapping_add(prev[3]),
chunk[4].wrapping_add(prev[4]),
chunk[5].wrapping_add(prev[5]),
];
*TryInto::<&mut [u8; 6]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Eight => {
let mut prev = [0; 8];
for chunk in current.chunks_exact_mut(8) {
let new_chunk = [
chunk[0].wrapping_add(prev[0]),
chunk[1].wrapping_add(prev[1]),
chunk[2].wrapping_add(prev[2]),
chunk[3].wrapping_add(prev[3]),
chunk[4].wrapping_add(prev[4]),
chunk[5].wrapping_add(prev[5]),
chunk[6].wrapping_add(prev[6]),
chunk[7].wrapping_add(prev[7]),
];
*TryInto::<&mut [u8; 8]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
},
Up => {
for (curr, &above) in current.iter_mut().zip(previous) {
*curr = curr.wrapping_add(above);
}
}
Avg if previous.is_empty() => match tbpp {
BytesPerPixel::One => {
current.iter_mut().reduce(|&mut prev, curr| {
*curr = curr.wrapping_add(prev / 2);
curr
});
}
BytesPerPixel::Two => {
let mut prev = [0; 2];
for chunk in current.chunks_exact_mut(2) {
let new_chunk = [
chunk[0].wrapping_add(prev[0] / 2),
chunk[1].wrapping_add(prev[1] / 2),
];
*TryInto::<&mut [u8; 2]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Three => {
let mut prev = [0; 3];
for chunk in current.chunks_exact_mut(3) {
let new_chunk = [
chunk[0].wrapping_add(prev[0] / 2),
chunk[1].wrapping_add(prev[1] / 2),
chunk[2].wrapping_add(prev[2] / 2),
];
*TryInto::<&mut [u8; 3]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Four => {
let mut prev = [0; 4];
for chunk in current.chunks_exact_mut(4) {
let new_chunk = [
chunk[0].wrapping_add(prev[0] / 2),
chunk[1].wrapping_add(prev[1] / 2),
chunk[2].wrapping_add(prev[2] / 2),
chunk[3].wrapping_add(prev[3] / 2),
];
*TryInto::<&mut [u8; 4]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Six => {
let mut prev = [0; 6];
for chunk in current.chunks_exact_mut(6) {
let new_chunk = [
chunk[0].wrapping_add(prev[0] / 2),
chunk[1].wrapping_add(prev[1] / 2),
chunk[2].wrapping_add(prev[2] / 2),
chunk[3].wrapping_add(prev[3] / 2),
chunk[4].wrapping_add(prev[4] / 2),
chunk[5].wrapping_add(prev[5] / 2),
];
*TryInto::<&mut [u8; 6]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
BytesPerPixel::Eight => {
let mut prev = [0; 8];
for chunk in current.chunks_exact_mut(8) {
let new_chunk = [
chunk[0].wrapping_add(prev[0] / 2),
chunk[1].wrapping_add(prev[1] / 2),
chunk[2].wrapping_add(prev[2] / 2),
chunk[3].wrapping_add(prev[3] / 2),
chunk[4].wrapping_add(prev[4] / 2),
chunk[5].wrapping_add(prev[5] / 2),
chunk[6].wrapping_add(prev[6] / 2),
chunk[7].wrapping_add(prev[7] / 2),
];
*TryInto::<&mut [u8; 8]>::try_into(chunk).unwrap() = new_chunk;
prev = new_chunk;
}
}
},
Avg => match tbpp {
BytesPerPixel::One => {
let mut lprev = [0; 1];
for (chunk, above) in current.chunks_exact_mut(1).zip(previous.chunks_exact(1)) {
let new_chunk =
[chunk[0].wrapping_add(((above[0] as u16 + lprev[0] as u16) / 2) as u8)];
*TryInto::<&mut [u8; 1]>::try_into(chunk).unwrap() = new_chunk;
lprev = new_chunk;
}
}
BytesPerPixel::Two => {
let mut lprev = [0; 2];
for (chunk, above) in current.chunks_exact_mut(2).zip(previous.chunks_exact(2)) {
let new_chunk = [
chunk[0].wrapping_add(((above[0] as u16 + lprev[0] as u16) / 2) as u8),
chunk[1].wrapping_add(((above[1] as u16 + lprev[1] as u16) / 2) as u8),
];
*TryInto::<&mut [u8; 2]>::try_into(chunk).unwrap() = new_chunk;
lprev = new_chunk;
}
}
BytesPerPixel::Three => {
let mut lprev = [0; 3];
for (chunk, above) in current.chunks_exact_mut(3).zip(previous.chunks_exact(3)) {
let new_chunk = [
chunk[0].wrapping_add(((above[0] as u16 + lprev[0] as u16) / 2) as u8),
chunk[1].wrapping_add(((above[1] as u16 + lprev[1] as u16) / 2) as u8),
chunk[2].wrapping_add(((above[2] as u16 + lprev[2] as u16) / 2) as u8),
];
*TryInto::<&mut [u8; 3]>::try_into(chunk).unwrap() = new_chunk;
lprev = new_chunk;
}
}
BytesPerPixel::Four => {
let mut lprev = [0; 4];
for (chunk, above) in current.chunks_exact_mut(4).zip(previous.chunks_exact(4)) {
let new_chunk = [
chunk[0].wrapping_add(((above[0] as u16 + lprev[0] as u16) / 2) as u8),
chunk[1].wrapping_add(((above[1] as u16 + lprev[1] as u16) / 2) as u8),
chunk[2].wrapping_add(((above[2] as u16 + lprev[2] as u16) / 2) as u8),
chunk[3].wrapping_add(((above[3] as u16 + lprev[3] as u16) / 2) as u8),
];
*TryInto::<&mut [u8; 4]>::try_into(chunk).unwrap() = new_chunk;
lprev = new_chunk;
}
}
BytesPerPixel::Six => {
let mut lprev = [0; 6];
for (chunk, above) in current.chunks_exact_mut(6).zip(previous.chunks_exact(6)) {
let new_chunk = [
chunk[0].wrapping_add(((above[0] as u16 + lprev[0] as u16) / 2) as u8),
chunk[1].wrapping_add(((above[1] as u16 + lprev[1] as u16) / 2) as u8),
chunk[2].wrapping_add(((above[2] as u16 + lprev[2] as u16) / 2) as u8),
chunk[3].wrapping_add(((above[3] as u16 + lprev[3] as u16) / 2) as u8),
chunk[4].wrapping_add(((above[4] as u16 + lprev[4] as u16) / 2) as u8),
chunk[5].wrapping_add(((above[5] as u16 + lprev[5] as u16) / 2) as u8),
];
*TryInto::<&mut [u8; 6]>::try_into(chunk).unwrap() = new_chunk;
lprev = new_chunk;
}
}
BytesPerPixel::Eight => {
let mut lprev = [0; 8];
for (chunk, above) in current.chunks_exact_mut(8).zip(previous.chunks_exact(8)) {
let new_chunk = [
chunk[0].wrapping_add(((above[0] as u16 + lprev[0] as u16) / 2) as u8),
chunk[1].wrapping_add(((above[1] as u16 + lprev[1] as u16) / 2) as u8),
chunk[2].wrapping_add(((above[2] as u16 + lprev[2] as u16) / 2) as u8),
chunk[3].wrapping_add(((above[3] as u16 + lprev[3] as u16) / 2) as u8),
chunk[4].wrapping_add(((above[4] as u16 + lprev[4] as u16) / 2) as u8),
chunk[5].wrapping_add(((above[5] as u16 + lprev[5] as u16) / 2) as u8),
chunk[6].wrapping_add(((above[6] as u16 + lprev[6] as u16) / 2) as u8),
chunk[7].wrapping_add(((above[7] as u16 + lprev[7] as u16) / 2) as u8),
];
*TryInto::<&mut [u8; 8]>::try_into(chunk).unwrap() = new_chunk;
lprev = new_chunk;
}
}
},
Paeth => {
// Paeth filter pixels:
// C B D
// A X
match tbpp {
BytesPerPixel::One => {
let mut a_bpp = [0; 1];
let mut c_bpp = [0; 1];
for (chunk, b_bpp) in current.chunks_exact_mut(1).zip(previous.chunks_exact(1))
{
let new_chunk = [chunk[0]
.wrapping_add(filter_paeth_decode(a_bpp[0], b_bpp[0], c_bpp[0]))];
*TryInto::<&mut [u8; 1]>::try_into(chunk).unwrap() = new_chunk;
a_bpp = new_chunk;
c_bpp = b_bpp.try_into().unwrap();
}
}
BytesPerPixel::Two => {
let mut a_bpp = [0; 2];
let mut c_bpp = [0; 2];
for (chunk, b_bpp) in current.chunks_exact_mut(2).zip(previous.chunks_exact(2))
{
let new_chunk = [
chunk[0]
.wrapping_add(filter_paeth_decode(a_bpp[0], b_bpp[0], c_bpp[0])),
chunk[1]
.wrapping_add(filter_paeth_decode(a_bpp[1], b_bpp[1], c_bpp[1])),
];
*TryInto::<&mut [u8; 2]>::try_into(chunk).unwrap() = new_chunk;
a_bpp = new_chunk;
c_bpp = b_bpp.try_into().unwrap();
}
}
BytesPerPixel::Three => {
#[cfg(feature = "unstable")]
simd::unfilter_paeth3(previous, current);
#[cfg(not(feature = "unstable"))]
{
let mut a_bpp = [0; 3];
let mut c_bpp = [0; 3];
for (chunk, b_bpp) in
current.chunks_exact_mut(3).zip(previous.chunks_exact(3))
{
let new_chunk = [
chunk[0].wrapping_add(filter_paeth_decode(
a_bpp[0], b_bpp[0], c_bpp[0],
)),
chunk[1].wrapping_add(filter_paeth_decode(
a_bpp[1], b_bpp[1], c_bpp[1],
)),
chunk[2].wrapping_add(filter_paeth_decode(
a_bpp[2], b_bpp[2], c_bpp[2],
)),
];
*TryInto::<&mut [u8; 3]>::try_into(chunk).unwrap() = new_chunk;
a_bpp = new_chunk;
c_bpp = b_bpp.try_into().unwrap();
}
}
}
BytesPerPixel::Four => {
#[cfg(feature = "unstable")]
simd::unfilter_paeth_u8::<4>(previous, current);
#[cfg(not(feature = "unstable"))]
{
let mut a_bpp = [0; 4];
let mut c_bpp = [0; 4];
for (chunk, b_bpp) in
current.chunks_exact_mut(4).zip(previous.chunks_exact(4))
{
let new_chunk = [
chunk[0].wrapping_add(filter_paeth_decode(
a_bpp[0], b_bpp[0], c_bpp[0],
)),
chunk[1].wrapping_add(filter_paeth_decode(
a_bpp[1], b_bpp[1], c_bpp[1],
)),
chunk[2].wrapping_add(filter_paeth_decode(
a_bpp[2], b_bpp[2], c_bpp[2],
)),
chunk[3].wrapping_add(filter_paeth_decode(
a_bpp[3], b_bpp[3], c_bpp[3],
)),
];
*TryInto::<&mut [u8; 4]>::try_into(chunk).unwrap() = new_chunk;
a_bpp = new_chunk;
c_bpp = b_bpp.try_into().unwrap();
}
}
}
BytesPerPixel::Six => {
#[cfg(feature = "unstable")]
simd::unfilter_paeth6(previous, current);
#[cfg(not(feature = "unstable"))]
{
let mut a_bpp = [0; 6];
let mut c_bpp = [0; 6];
for (chunk, b_bpp) in
current.chunks_exact_mut(6).zip(previous.chunks_exact(6))
{
let new_chunk = [
chunk[0].wrapping_add(filter_paeth_decode(
a_bpp[0], b_bpp[0], c_bpp[0],
)),
chunk[1].wrapping_add(filter_paeth_decode(
a_bpp[1], b_bpp[1], c_bpp[1],
)),
chunk[2].wrapping_add(filter_paeth_decode(
a_bpp[2], b_bpp[2], c_bpp[2],
)),
chunk[3].wrapping_add(filter_paeth_decode(
a_bpp[3], b_bpp[3], c_bpp[3],
)),
chunk[4].wrapping_add(filter_paeth_decode(
a_bpp[4], b_bpp[4], c_bpp[4],
)),
chunk[5].wrapping_add(filter_paeth_decode(
a_bpp[5], b_bpp[5], c_bpp[5],
)),
];
*TryInto::<&mut [u8; 6]>::try_into(chunk).unwrap() = new_chunk;
a_bpp = new_chunk;
c_bpp = b_bpp.try_into().unwrap();
}
}
}
BytesPerPixel::Eight => {
#[cfg(feature = "unstable")]
simd::unfilter_paeth_u8::<8>(previous, current);
#[cfg(not(feature = "unstable"))]
{
let mut a_bpp = [0; 8];
let mut c_bpp = [0; 8];
for (chunk, b_bpp) in
current.chunks_exact_mut(8).zip(previous.chunks_exact(8))
{
let new_chunk = [
chunk[0].wrapping_add(filter_paeth_decode(
a_bpp[0], b_bpp[0], c_bpp[0],
)),
chunk[1].wrapping_add(filter_paeth_decode(
a_bpp[1], b_bpp[1], c_bpp[1],
)),
chunk[2].wrapping_add(filter_paeth_decode(
a_bpp[2], b_bpp[2], c_bpp[2],
)),
chunk[3].wrapping_add(filter_paeth_decode(
a_bpp[3], b_bpp[3], c_bpp[3],
)),
chunk[4].wrapping_add(filter_paeth_decode(
a_bpp[4], b_bpp[4], c_bpp[4],
)),
chunk[5].wrapping_add(filter_paeth_decode(
a_bpp[5], b_bpp[5], c_bpp[5],
)),
chunk[6].wrapping_add(filter_paeth_decode(
a_bpp[6], b_bpp[6], c_bpp[6],
)),
chunk[7].wrapping_add(filter_paeth_decode(
a_bpp[7], b_bpp[7], c_bpp[7],
)),
];
*TryInto::<&mut [u8; 8]>::try_into(chunk).unwrap() = new_chunk;
a_bpp = new_chunk;
c_bpp = b_bpp.try_into().unwrap();
}
}
}
}
}
}
}
fn filter_internal(
method: FilterType,
bpp: usize,
len: usize,
previous: &[u8],
current: &[u8],
output: &mut [u8],
) -> FilterType {
use self::FilterType::*;
// This value was chosen experimentally based on what achieved the best performance. The
// Rust compiler does auto-vectorization, and 32-bytes per loop iteration seems to enable
// the fastest code when doing so.
const CHUNK_SIZE: usize = 32;
match method {
NoFilter => {
output.copy_from_slice(current);
NoFilter
}
Sub => {
let mut out_chunks = output[bpp..].chunks_exact_mut(CHUNK_SIZE);
let mut cur_chunks = current[bpp..].chunks_exact(CHUNK_SIZE);
let mut prev_chunks = current[..len - bpp].chunks_exact(CHUNK_SIZE);
for ((out, cur), prev) in (&mut out_chunks).zip(&mut cur_chunks).zip(&mut prev_chunks) {
for i in 0..CHUNK_SIZE {
out[i] = cur[i].wrapping_sub(prev[i]);
}
}
for ((out, cur), &prev) in out_chunks
.into_remainder()
.iter_mut()
.zip(cur_chunks.remainder())
.zip(prev_chunks.remainder())
{
*out = cur.wrapping_sub(prev);
}
output[..bpp].copy_from_slice(¤t[..bpp]);
Sub
}
Up => {
let mut out_chunks = output.chunks_exact_mut(CHUNK_SIZE);
let mut cur_chunks = current.chunks_exact(CHUNK_SIZE);
let mut prev_chunks = previous.chunks_exact(CHUNK_SIZE);
for ((out, cur), prev) in (&mut out_chunks).zip(&mut cur_chunks).zip(&mut prev_chunks) {
for i in 0..CHUNK_SIZE {
out[i] = cur[i].wrapping_sub(prev[i]);
}
}
for ((out, cur), &prev) in out_chunks
.into_remainder()
.iter_mut()
.zip(cur_chunks.remainder())
.zip(prev_chunks.remainder())
{
*out = cur.wrapping_sub(prev);
}
Up
}
Avg => {
let mut out_chunks = output[bpp..].chunks_exact_mut(CHUNK_SIZE);
let mut cur_chunks = current[bpp..].chunks_exact(CHUNK_SIZE);
let mut cur_minus_bpp_chunks = current[..len - bpp].chunks_exact(CHUNK_SIZE);
let mut prev_chunks = previous[bpp..].chunks_exact(CHUNK_SIZE);
for (((out, cur), cur_minus_bpp), prev) in (&mut out_chunks)
.zip(&mut cur_chunks)
.zip(&mut cur_minus_bpp_chunks)
.zip(&mut prev_chunks)
{
for i in 0..CHUNK_SIZE {
// Bitwise average of two integers without overflow and
// without converting to a wider bit-width. See:
// http://aggregate.org/MAGIC/#Average%20of%20Integers
// If this is unrolled by component, consider reverting to
// `((cur_minus_bpp[i] as u16 + prev[i] as u16) / 2) as u8`
out[i] = cur[i].wrapping_sub(
(cur_minus_bpp[i] & prev[i]) + ((cur_minus_bpp[i] ^ prev[i]) >> 1),
);
}
}
for (((out, cur), &cur_minus_bpp), &prev) in out_chunks
.into_remainder()
.iter_mut()
.zip(cur_chunks.remainder())
.zip(cur_minus_bpp_chunks.remainder())
.zip(prev_chunks.remainder())
{
*out = cur.wrapping_sub((cur_minus_bpp & prev) + ((cur_minus_bpp ^ prev) >> 1));
}
for i in 0..bpp {
output[i] = current[i].wrapping_sub(previous[i] / 2);
}
Avg
}
Paeth => {
let mut out_chunks = output[bpp..].chunks_exact_mut(CHUNK_SIZE);
let mut cur_chunks = current[bpp..].chunks_exact(CHUNK_SIZE);
let mut a_chunks = current[..len - bpp].chunks_exact(CHUNK_SIZE);
let mut b_chunks = previous[bpp..].chunks_exact(CHUNK_SIZE);
let mut c_chunks = previous[..len - bpp].chunks_exact(CHUNK_SIZE);
for ((((out, cur), a), b), c) in (&mut out_chunks)
.zip(&mut cur_chunks)
.zip(&mut a_chunks)
.zip(&mut b_chunks)
.zip(&mut c_chunks)
{
for i in 0..CHUNK_SIZE {
out[i] = cur[i].wrapping_sub(filter_paeth(a[i], b[i], c[i]));
}
}
for ((((out, cur), &a), &b), &c) in out_chunks
.into_remainder()
.iter_mut()
.zip(cur_chunks.remainder())
.zip(a_chunks.remainder())
.zip(b_chunks.remainder())
.zip(c_chunks.remainder())
{
*out = cur.wrapping_sub(filter_paeth(a, b, c));
}
for i in 0..bpp {
output[i] = current[i].wrapping_sub(filter_paeth(0, previous[i], 0));
}
Paeth
}
}
}
pub(crate) fn filter(
method: FilterType,
adaptive: AdaptiveFilterType,
bpp: BytesPerPixel,
previous: &[u8],
current: &[u8],
output: &mut [u8],
) -> FilterType {
use FilterType::*;
let bpp = bpp.into_usize();
let len = current.len();
match adaptive {
AdaptiveFilterType::NonAdaptive => {
filter_internal(method, bpp, len, previous, current, output)
}
AdaptiveFilterType::Adaptive => {
let mut min_sum: u64 = u64::MAX;
let mut filter_choice = FilterType::NoFilter;
for &filter in [Sub, Up, Avg, Paeth].iter() {
filter_internal(filter, bpp, len, previous, current, output);
let sum = sum_buffer(output);
if sum <= min_sum {
min_sum = sum;
filter_choice = filter;
}
}
if filter_choice != Paeth {
filter_internal(filter_choice, bpp, len, previous, current, output);
}
filter_choice
}
}
}
// Helper function for Adaptive filter buffer summation
fn sum_buffer(buf: &[u8]) -> u64 {
const CHUNK_SIZE: usize = 32;
let mut buf_chunks = buf.chunks_exact(CHUNK_SIZE);
let mut sum = 0_u64;
for chunk in &mut buf_chunks {
// At most, `acc` can be `32 * (i8::MIN as u8) = 32 * 128 = 4096`.
let mut acc = 0;
for &b in chunk {
acc += u64::from((b as i8).unsigned_abs());
}
sum = sum.saturating_add(acc);
}
let mut acc = 0;
for &b in buf_chunks.remainder() {
acc += u64::from((b as i8).unsigned_abs());
}
sum.saturating_add(acc)
}
#[cfg(test)]
mod test {
use super::{filter, unfilter, AdaptiveFilterType, BytesPerPixel, FilterType};
use core::iter;
#[test]
fn roundtrip() {
// A multiple of 8, 6, 4, 3, 2, 1
const LEN: u8 = 240;
let previous: Vec<_> = iter::repeat(1).take(LEN.into()).collect();
let current: Vec<_> = (0..LEN).collect();
let expected = current.clone();
let adaptive = AdaptiveFilterType::NonAdaptive;
let roundtrip = |kind, bpp: BytesPerPixel| {
let mut output = vec![0; LEN.into()];
filter(kind, adaptive, bpp, &previous, ¤t, &mut output);
unfilter(kind, bpp, &previous, &mut output);
assert_eq!(
output, expected,
"Filtering {:?} with {:?} does not roundtrip",
bpp, kind
);
};
let filters = [
FilterType::NoFilter,
FilterType::Sub,
FilterType::Up,
FilterType::Avg,
FilterType::Paeth,
];
let bpps = [
BytesPerPixel::One,
BytesPerPixel::Two,
BytesPerPixel::Three,
BytesPerPixel::Four,
BytesPerPixel::Six,
BytesPerPixel::Eight,
];
for &filter in filters.iter() {
for &bpp in bpps.iter() {
roundtrip(filter, bpp);
}
}
}
#[test]
fn roundtrip_ascending_previous_line() {
// A multiple of 8, 6, 4, 3, 2, 1
const LEN: u8 = 240;
let previous: Vec<_> = (0..LEN).collect();
let current: Vec<_> = (0..LEN).collect();
let expected = current.clone();
let adaptive = AdaptiveFilterType::NonAdaptive;
let roundtrip = |kind, bpp: BytesPerPixel| {
let mut output = vec![0; LEN.into()];
filter(kind, adaptive, bpp, &previous, ¤t, &mut output);
unfilter(kind, bpp, &previous, &mut output);
assert_eq!(
output, expected,
"Filtering {:?} with {:?} does not roundtrip",
bpp, kind
);
};
let filters = [
FilterType::NoFilter,
FilterType::Sub,
FilterType::Up,
FilterType::Avg,
FilterType::Paeth,
];
let bpps = [
BytesPerPixel::One,
BytesPerPixel::Two,
BytesPerPixel::Three,
BytesPerPixel::Four,
BytesPerPixel::Six,
BytesPerPixel::Eight,
];
for &filter in filters.iter() {
for &bpp in bpps.iter() {
roundtrip(filter, bpp);
}
}
}
#[test]
// This tests that converting u8 to i8 doesn't overflow when taking the
// absolute value for adaptive filtering: -128_i8.abs() will panic in debug
// or produce garbage in release mode. The sum of 0..=255u8 should equal the
// sum of the absolute values of -128_i8..=127, or abs(-128..=0) + 1..=127.
fn sum_buffer_test() {
let sum = (0..=128).sum::<u64>() + (1..=127).sum::<u64>();
let buf: Vec<u8> = (0_u8..=255).collect();
assert_eq!(sum, crate::filter::sum_buffer(&buf));
}
}