基于jpeg算法的图像压缩解压缩matlab仿真+仿真操作录像

clc; clear; close all; warning off; addpath(genpath(pwd)); %% Get the basis functions once basis = get_basis(); %% Get the grayscale image img = imread('B2DBY.jpg'); img = double(rgb2gray(img)); %convert to grayscale image %% construct square matrix whose rows and columns divisable by 8 [img, rows, cols] = convert_dimensions(img,8); %% DCT freq_img = image_dct(img,basis); %% Quantization q = [ 1 2 4 8 16 32 64 128; 2 4 4 8 16 32 64 128; 4 4 8 16 32 64 128 128; 8 8 16 32 64 128 128 256; 16 16 32 64 128 128 256 256; 32 32 64 128 128 256 256 256; 64 64 128 128 256 256 256 256; 128 128 128 256 256 256 256 256]; quant_img = special_quantize(freq_img,q); %% Transform 2D to 1D & Run length Encoding q_img_1D=[]; for n_row=1:rows/8 for n_col=1:cols/8 n_start_row = 8*(n_row-1)+1; n_start_col = 8*(n_col-1)+1; block = quant_img(n_start_row:n_start_row+7,n_start_col:n_start_col+7); block_1D = run_length_encoding(block); q_img_1D = [q_img_1D block_1D]; end end %% Huffman Step Prs = get_probabilities(q_img_1D); dict2 = jpeg_huffman_table(Prs); encoded_stream = jpeg_huffman_encoder(dict2,q_img_1D); %code = huffmanenco(q_img_1D,dict); %% Huffman decoder decoded_stream = jpeg_huffman_decoder(dict2,encoded_stream); %decoded_stream = huffmandeco(code,dict); %% From 1D to 2D decoded_img = run_length_decoding(decoded_stream,size(img)); %% Inverse Quantization decoded_img = inverse_quantization(decoded_img, q); %% Inverse-DCT decoded_img = image_idct(decoded_img,basis); %% Quality Metrics RMS_error = sqrt(sum((img-decoded_img).^2,'all')./(rows*cols)); compression_ratio = (8*rows*cols)/length(encoded_stream)*100; %% Showing Time! figure (1) imshow(uint8(img)); figure (2) imshow(uint8(decoded_img));