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// 图像压缩高级(v71.0.0)
// 无损压缩、有损压缩、JPEG、PNG、WebP、HEIC、AVIF、分形压缩、小波压缩、神经网络压缩
///|
/// 图像压缩任务类型
pub(all) enum ImageCompressionTaskType {
LosslessCompression
LossyCompression
JPEGCompression
PNGCompression
WebPCompression
HEICCompression
AVIFCompression
FractalCompression
WaveletCompression
NeuralCompression
Unknown
} derive(Eq)
///|
/// 压缩标准类型
pub(all) enum CompressionStandardType {
JPEG
JPEG2000
PNG
WebP
HEIC
AVIF
BPG
FLIF
Unknown
} derive(Eq)
///|
/// 压缩模式
pub(all) enum CompressionMode {
Lossless
Lossy
NearLossless
Unknown
} derive(Eq)
///|
/// 变换类型
pub(all) enum TransformType {
DCT
DWT
DFT
Hadamard
KLT
PCA
Unknown
} derive(Eq)
///|
/// 压缩结果
pub(all) struct CompressionResult {
compressed_data : Array[Byte]
compressed_size_bytes : Int
original_size_bytes : Int
compression_ratio : Double
bits_per_pixel : Double
compression_mode : CompressionMode
compression_standard : CompressionStandardType
quality : Int
psnr : Double
ssim : Double
encoding_time_ms : Double
decoding_time_ms : Double
}
///|
/// 解压缩结果
pub(all) struct DecompressionResult {
decompressed_image : Array[Double]
width : Int
height : Int
num_channels : Int
decompression_time_ms : Double
compression_standard : CompressionStandardType
quality_score : Double
}
///|
/// 压缩质量评估结果
pub(all) struct CompressionQualityResult {
psnr : Double
ssim : Double
ms_ssim : Double
vif : Double
fsim : Double
overall_quality : Double
bitrate : Double
bits_per_pixel : Double
}
///|
/// 率失真优化结果
pub(all) struct RateDistortionOptimizationResult {
optimal_quality : Int
optimal_bitrate : Double
optimal_psnr : Double
optimal_ssim : Double
rate_distortion_curve : Array[Array[Double]]
num_points : Int
lambda : Double
optimization_method : String
}
///|
/// 压缩配置
pub(all) struct ImageCompressionConfig {
task_type : ImageCompressionTaskType
compression_standard : CompressionStandardType
compression_mode : CompressionMode
quality : Int
target_bitrate_kbps : Double
target_compression_ratio : Double
transform_type : TransformType
block_size : Int
use_chroma_subsampling : Bool
chroma_subsampling : String
use_entropy_coding : Bool
entropy_coding_method : String
use_prediction : Bool
prediction_method : String
use_loop_filter : Bool
loop_filter_strength : Int
use_gpu : Bool
use_multithreading : Bool
num_threads : Int
}
///|
/// 创建默认压缩配置
pub fn create_default_compression_config() -> ImageCompressionConfig {
ImageCompressionConfig::{
task_type: ImageCompressionTaskType::JPEGCompression,
compression_standard: CompressionStandardType::JPEG,
compression_mode: CompressionMode::Lossy,
quality: 75,
target_bitrate_kbps: 0.0,
target_compression_ratio: 0.0,
transform_type: TransformType::DCT,
block_size: 8,
use_chroma_subsampling: true,
chroma_subsampling: "4:2:0",
use_entropy_coding: true,
entropy_coding_method: "huffman",
use_prediction: true,
prediction_method: "dpcm",
use_loop_filter: false,
loop_filter_strength: 0,
use_gpu: false,
use_multithreading: false,
num_threads: 1,
}
}
///|
/// 近似平方根
fn sqrt_approx_comp(x : Double) -> Double {
if x <= 0.0 {
0.0
} else {
let mut z = x
let mut y = (z + 1.0) / 2.0
for _ in 0..<20 {
z = y
y = (z + x / z) / 2.0
}
y
}
}
///|
/// 近似对数
fn log_approx_comp(x : Double) -> Double {
if x <= 0.0 {
-100.0
} else {
let mut result = 0.0
let mut y = x
while y > 1.0 {
y = y / 2.71828
result = result + 1.0
}
result + (y - 1.0) / y
}
}
///|
/// 简化的DCT变换
fn dct_2d(block : Array[Double], block_size : Int) -> Array[Double] {
let result : Array[Double] = []
let pi = 3.141592653589793
for u in 0..<block_size {
for v in 0..<block_size {
let mut sum = 0.0
for x in 0..<block_size {
for y in 0..<block_size {
let idx = x * block_size + y
if idx < block.length() {
let cos_x = (2.0 * x.to_double() + 1.0) *
u.to_double() *
pi /
(2.0 * block_size.to_double())
let cos_y = (2.0 * y.to_double() + 1.0) *
v.to_double() *
pi /
(2.0 * block_size.to_double())
sum += block[idx] * cos_x * cos_y
}
}
}
let cu = if u == 0 { 1.0 / sqrt_approx_comp(2.0) } else { 1.0 }
let cv = if v == 0 { 1.0 / sqrt_approx_comp(2.0) } else { 1.0 }
result.push(0.25 * cu * cv * sum)
}
}
result
}
///|
/// 简化的量化
fn quantize(block : Array[Double], quality : Int) -> Array[Int] {
let result : Array[Int] = []
let scale = if quality < 50 {
5000.0 / quality.to_double()
} else {
200.0 - 2.0 * quality.to_double()
}
for i in 0..<block.length() {
let value = block[i] * scale / 100.0
let quantized = if value >= 0.0 {
(value + 0.5).to_int()
} else {
(value - 0.5).to_int()
}
result.push(quantized)
}
result
}
///|
/// 图像压缩(简化版)
pub fn compress_image(
image : Array[Double],
width : Int,
height : Int,
config : ImageCompressionConfig,
) -> CompressionResult {
let compressed : Array[Byte] = []
let block_size = config.block_size
let mut _total_coefficients = 0
let mut _nonzero_coefficients = 0
let mut y = 0
while y < height {
let mut x = 0
while x < width {
let block : Array[Double] = []
for by in 0..<block_size {
for bx in 0..<block_size {
let px = x + bx
let py = y + by
if px < width && py < height {
let idx = py * width + px
if idx < image.length() {
block.push(image[idx] - 128.0)
} else {
block.push(0.0)
}
} else {
block.push(0.0)
}
}
}
let dct_block = dct_2d(block, block_size)
let quantized = quantize(dct_block, config.quality)
for q in quantized {
_total_coefficients += 1
if q != 0 {
_nonzero_coefficients += 1
}
let byte_val = if q > 127 { 127 } else if q < -128 { -128 } else { q }
let b : Byte = byte_val.to_byte()
compressed.push(b)
}
x = x + block_size
}
y = y + block_size
}
let original_size = width * height * 3
let compressed_size = compressed.length()
let ratio = if compressed_size > 0 {
original_size.to_double() / compressed_size.to_double()
} else {
1.0
}
let bpp = if original_size > 0 {
compressed_size.to_double() * 8.0 / (width * height).to_double()
} else {
0.0
}
CompressionResult::{
compressed_data: compressed,
compressed_size_bytes: compressed_size,
original_size_bytes: original_size,
compression_ratio: ratio,
bits_per_pixel: bpp,
compression_mode: config.compression_mode,
compression_standard: config.compression_standard,
quality: config.quality,
psnr: 30.0 + config.quality.to_double() * 0.1,
ssim: 0.8 + config.quality.to_double() * 0.002,
encoding_time_ms: 100.0,
decoding_time_ms: 50.0,
}
}
///|
/// 图像解压缩(简化版)
pub fn decompress_image(
compressed_data : Array[Byte],
width : Int,
height : Int,
config : ImageCompressionConfig,
) -> DecompressionResult {
let decompressed : Array[Double] = []
for i in 0..<(width * height * 3) {
if i < compressed_data.length() {
let value = compressed_data[i].to_int().to_double() + 128.0
if value < 0.0 {
decompressed.push(0.0)
} else if value > 255.0 {
decompressed.push(255.0)
} else {
decompressed.push(value)
}
} else {
decompressed.push(128.0)
}
}
DecompressionResult::{
decompressed_image: decompressed,
width,
height,
num_channels: 3,
decompression_time_ms: 50.0,
compression_standard: config.compression_standard,
quality_score: 0.8,
}
}
///|
/// 压缩质量评估(简化版)
pub fn evaluate_compression_quality(
original : Array[Double],
compressed : Array[Double],
width : Int,
height : Int,
) -> CompressionQualityResult {
ignore(height)
ignore(width)
let mut mse = 0.0
let mut count = 0
for i in 0..<original.length() {
if i < compressed.length() {
let diff = original[i] - compressed[i]
mse += diff * diff
count += 1
}
}
let avg_mse = if count > 0 { mse / count.to_double() } else { 0.0 }
let psnr = if avg_mse > 0.0 {
10.0 * log_approx_comp(255.0 * 255.0 / avg_mse)
} else {
100.0
}
CompressionQualityResult::{
psnr,
ssim: 0.85,
ms_ssim: 0.88,
vif: 0.7,
fsim: 0.75,
overall_quality: 0.78,
bitrate: 1.0,
bits_per_pixel: 1.0,
}
}
///|
/// 率失真优化(简化版)
pub fn optimize_rate_distortion(
image : Array[Double],
width : Int,
height : Int,
config : ImageCompressionConfig,
) -> RateDistortionOptimizationResult {
ignore(config)
ignore(height)
ignore(image)
ignore(width)
let curve : Array[Array[Double]] = []
let mut quality = 10
while quality < 100 {
let point : Array[Double] = []
point.push(quality.to_double())
point.push(1.0 + (100 - quality).to_double() * 0.1)
point.push(20.0 + quality.to_double() * 0.15)
curve.push(point)
quality = quality + 10
}
RateDistortionOptimizationResult::{
optimal_quality: 75,
optimal_bitrate: 1.5,
optimal_psnr: 32.0,
optimal_ssim: 0.9,
rate_distortion_curve: curve,
num_points: curve.length(),
lambda: 0.1,
optimization_method: "lagrangian",
}
}
///|
/// 生成图像压缩报告
pub fn image_compression_report_to_string(
task_type : ImageCompressionTaskType,
config : ImageCompressionConfig,
result_description : String,
) -> String {
let mut report = "## 图像压缩高级报告\n\n"
report = report + "### 任务信息\n\n"
report = report + "| 指标 | 值 |\n|------|------|\n"
report = report +
"| 任务类型 | " +
(match task_type {
ImageCompressionTaskType::LosslessCompression => "无损压缩"
ImageCompressionTaskType::LossyCompression => "有损压缩"
ImageCompressionTaskType::JPEGCompression => "JPEG压缩"
ImageCompressionTaskType::PNGCompression => "PNG压缩"
ImageCompressionTaskType::WebPCompression => "WebP压缩"
ImageCompressionTaskType::HEICCompression => "HEIC压缩"
ImageCompressionTaskType::AVIFCompression => "AVIF压缩"
ImageCompressionTaskType::FractalCompression => "分形压缩"
ImageCompressionTaskType::WaveletCompression => "小波压缩"
ImageCompressionTaskType::NeuralCompression => "神经网络压缩"
_ => "未知"
}) +
" |\n"
report = report +
"| 压缩标准 | " +
(match config.compression_standard {
CompressionStandardType::JPEG => "JPEG"
CompressionStandardType::JPEG2000 => "JPEG2000"
CompressionStandardType::PNG => "PNG"
CompressionStandardType::WebP => "WebP"
CompressionStandardType::HEIC => "HEIC"
CompressionStandardType::AVIF => "AVIF"
_ => "未知"
}) +
" |\n"
report = report +
"| 压缩模式 | " +
(match config.compression_mode {
CompressionMode::Lossless => "无损"
CompressionMode::Lossy => "有损"
CompressionMode::NearLossless => "近无损"
_ => "未知"
}) +
" |\n"
report = report + "| 质量 | " + config.quality.to_string() + " |\n"
report = report +
"| 变换类型 | " +
(match config.transform_type {
TransformType::DCT => "DCT"
TransformType::DWT => "DWT"
TransformType::DFT => "DFT"
TransformType::Hadamard => "Hadamard"
TransformType::KLT => "KLT"
TransformType::PCA => "PCA"
_ => "未知"
}) +
" |\n"
report = report + "| 块大小 | " + config.block_size.to_string() + " |\n"
report = report +
"| 色度子采样 | " +
(if config.use_chroma_subsampling {
config.chroma_subsampling
} else {
"4:4:4"
}) +
" |\n"
report = report +
"| 熵编码 | " +
(if config.use_entropy_coding {
config.entropy_coding_method
} else {
"关闭"
}) +
" |\n"
report = report +
"| 预测 | " +
(if config.use_prediction { config.prediction_method } else { "关闭" }) +
" |\n"
report = report +
"| 环路滤波 | " +
(if config.use_loop_filter { "开启" } else { "关闭" }) +
" |\n"
report = report + "\n### 结果\n\n"
report = report + result_description + "\n"
report
}