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refactor dct and quality

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This commit is contained in:
Mark Hoekveen
2024-05-08 10:12:53 +02:00
parent 31351ea91d
commit 1aa32a3788
3 changed files with 68 additions and 62 deletions
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src/descriptors/dct.rs
+59 -55
View File
@@ -2,14 +2,9 @@ use image::{save_buffer, GenericImageView};
use std::f64::consts::{PI, SQRT_2};
use crate::descriptors::{Descriptor, print_matrix, DCT};
/// A struct representing a Discrete Cosine Transform descriptor
/// Transforms an image into an 8x8 grayscale version, and transforms it
/// into the frequency domain
// pub struct DCT {
// quantization_matrix: [u8; 64],
// }
impl DCT {
/// Returns a new DCT instance with a calculated DCT matrix.
pub fn new() -> DCT {
let base_quantization_matrix: [u8; 64] = [
16, 11, 10, 16, 24, 40, 51, 61,
@@ -23,8 +18,10 @@ impl DCT {
];
DCT { quantization_matrix: base_quantization_matrix }
}
pub fn quantization_matrix(&self, quality: u8) -> [u8; 64] {
let mut quantization_matrix = self.quantization_matrix.clone();
// Builds DCT with given quality value
pub fn with_quality(mut self, quality: u8) -> Self {
let mut quantization_matrix: [u8; 64] = [0; 64];
let scalar: f32 = match quality {
1..=49 => 5000.0/quality as f32,
50..=100 => 200.0 - 2.0*quality as f32,
@@ -36,54 +33,14 @@ impl DCT {
quantization_matrix[i] = 1;
}
}
quantization_matrix
self.quantization_matrix = quantization_matrix;
self
}
/// TODO
// pub fn dct(&self, img: image::DynamicImage) -> [f64; 64] {
// }
pub fn idct(&self, dct_values: [f64; 64]) -> [u8; 64] {
let mut reconstructed: [u8; 64] = [0; 64];
for k in 0..64 {
let x = (k%8) as f64;
let y = (k/8) as f64;
let mut sum = 0.0;
for u in 0..8 {
for v in 0..8 {
let mut alpha = 1.0;
if u == 0 {
alpha = alpha / SQRT_2
}
if v == 0 {
alpha = alpha / SQRT_2
}
let uv = (v*8)+u;
let v = v as f64;
let u = u as f64;
sum +=
alpha *
dct_values[uv] *
((2.0 * x + 1.0) * u * PI / 16.0).cos() *
((2.0 * y + 1.0) * v * PI / 16.0).cos()
}
}
sum = 127.0 + (0.25 * sum).round();
reconstructed[k] = std::cmp::min(255_u8, sum as u8);
}
reconstructed
}
}
impl Descriptor for DCT {
fn describe(&self, img: image::DynamicImage) -> u64 {
let quality: u8 = 15;
let qmatrix = self.quantization_matrix(quality);
println!("Base quantization matrix:\n{}", print_matrix(self.quantization_matrix));
println!("Q-{} quantization matrix:\n{}", quality, print_matrix(qmatrix));
pub fn dct(&self, img: image::DynamicImage) -> [f64; 64] {
//let qmatrix = self.quantization_matrix(self.quality);
//println!("Q-{} quantization matrix:\n{}", self.quality, print_matrix(qmatrix));
let mut dct_values: [f64; 64] = [0.0; 64];
let img = self.resize(img);
img.save("resize.png").expect("Error saving file");
for u in 0..8 {
for v in 0..8 {
@@ -111,19 +68,64 @@ impl Descriptor for DCT {
//println!{"{}", dct_values[k]}
}
}
dct_values
}
pub fn idct(&self, dct_values: [f64; 64]) -> [u8; 64] {
let mut reconstructed: [u8; 64] = [0; 64];
for k in 0..64 {
let x = (k%8) as f64;
let y = (k/8) as f64;
let mut sum = 0.0;
for u in 0..8 {
for v in 0..8 {
let mut alpha = 1.0;
if u == 0 {
alpha = alpha / SQRT_2
}
if v == 0 {
alpha = alpha / SQRT_2
}
let uv = (v*8)+u;
let v = v as f64;
let u = u as f64;
sum +=
alpha *
dct_values[uv] *
((2.0 * x + 1.0) * u * PI / 16.0).cos() *
((2.0 * y + 1.0) * v * PI / 16.0).cos();
}
}
sum = 127.0 + (0.25 * sum).round();
reconstructed[k] = std::cmp::min(255_u8, sum as u8);
}
reconstructed
}
}
impl Descriptor for DCT {
fn describe(&self, img: image::DynamicImage) -> u64 {
let img = self.resize(img);
let mut dct_values = self.dct(img);
println!("DCT-coefficients:\n {}", print_matrix(dct_values));
// Quantization:
println!("Using quantization matrix:\n{}", print_matrix(self.quantization_matrix));
for i in 0..64 {
dct_values[i] = (dct_values[i] / qmatrix[i] as f64).round();
dct_values[i] = (dct_values[i] / self.quantization_matrix[i] as f64).round();
}
println!("DCT-coefficients, quantized:\n {}", print_matrix(dct_values));
// De-quantization:
for i in 0..64 {
dct_values[i] = dct_values[i] * qmatrix[i] as f64;
dct_values[i] = dct_values[i] * self.quantization_matrix[i] as f64;
}
println!("DCT-coefficients, de-quantized:\n {}", print_matrix(dct_values));
// Reconstruction original pixel values:
let reconstructed = self.idct(dct_values);
println!("Reconstructed pixel values:\n{}", print_matrix(reconstructed));
save_buffer(
"reconstructed.png",
&reconstructed,
@@ -131,6 +133,8 @@ impl Descriptor for DCT {
8,
image::ColorType::L8
).expect("Error saving buffer");
// Calculating descriptor from dct values:
let mut mask: u64 = 0;
for dct in dct_values {
if dct > 0.0 {
src/descriptors/mod.rs
+8 -5
View File
@@ -13,22 +13,25 @@ pub trait Descriptor {
/// Interprets a 64-element array as an 8x8 matrix
/// returns a nicely printable string
fn print_matrix<T: ToString>(array: [T; 64]) -> String {
fn print_matrix<T: ToString + std::fmt::Display>(array: [T; 64]) -> String {
let mut output = String::new();
for x in 0..8 {
for y in 0..8 {
output.push_str(&array[x*8+y].to_string());
output.push_str(",\t");
let element = format!("{:.2}", &array[x*8+y]);
let element = format!("{:8}\t", element);
output.push_str(&element);
}
output.push('\n');
}
output
}
/// A struct representing a Discrete Cosine Transform descriptor
/// Transforms an image into an 8x8 grayscale version, and transforms it
/// into the frequency domain
pub struct DCT {
quantization_matrix: [u8; 64],
quantization_matrix: [u8; 64]
}
pub mod dct;
// fn median64<T: Ord + Copy>(values: &[T]) -> T {