基于ffmpeg的图像压缩与处理

import cv2 import numpy as np path = "150.png" figure = cv2.imread(path) # 提取出RGB并根据公式转化为YUV b = figure[:,:,0] g = figure[:,:,1] r = figure[:,:,2] y = 0.275 * r + 0.504 * g + 0.098 * b + 16 u = -0.148 * r - 0.291 * g + 0.439 * b + 128 v = 0.439 * r - 0.368 * g - 0.071 * b + 128 # print(y) # 调用cv2的内置函数进行dct变化 y_dct = cv2.dct(y) u_dct = cv2.dct(u) v_dct = cv2.dct(v) # print(y_dct) # 两个量化表 co1 = [[17,18,24,47,99,99,99,99],[18,21,26,66,99,99,99,99], [24,26,59,99,99,99,99,99],[47,66,99,99,99,99,99,99], [99,99,99,99,99,99,99,99],[99,99,99,99,99,99,99,99], [99,99,99,99,99,99,99,99],[99,99,99,99,99,99,99,99]] co2 = [[16,11,10,16,24,40,51,61],[12,12,14,19,26,58,60,55], [14,13,16,24,40,57,69,56],[14,17,22,29,51,87,80,62], [18,22,37,56,68,109,103,77],[24,35,55,64,81,104,113,92], [49,64,78,87,103,121,120,101],[97,92,95,98,112,100,103,99]] # 对每个8*8的图像块进行进行量化操作 y_q = np.zeros((len(y_dct),len(y_dct[0]))) u_q = np.zeros((len(u_dct),len(u_dct[0]))) v_q = np.zeros((len(v_dct),len(v_dct[0]))) for i in range(len(y_dct)): for j in range(len(y_dct[0])): x = i % 8 y = j % 8 y_q[i][j] = int(y_dct[i][j] / co1[x][y]) u_q[i][j] = int(u_dct[i][j] / co2[x][y]) v_q[i][j] = int(v_dct[i][j] / co2[x][y]) # print(y_q) # 进行Z字型编码 row,column = len(y_q),len(y_q[0]) print(row,column) z_y = [] z_u = [] z_v = [] length = column + row x = y = 0 for i in range(length): if i < column: if i % 2: # 奇数，向下走 while x <= row-1 and y >= 0: z_y.append(y_q[x][y]) z_u.append(u_q[x][y]) z_v.append(v_q[x][y]) x += 1 y -= 1 y = 0 if x == 368: x -= 1 else: # 偶数，向上走 while x >= 0 and y <= column-1: z_y.append(y_q[x][y]) z_u.append(u_q[x][y]) z_v.append(v_q[x][y]) x -= 1 y += 1 x = 0 else: if i % 2: # 奇数，向下走 while x <= row-1 and y <= column-1: z_y.append(y_q[x][y]) z_u.append(u_q[x][y]) z_v.append(v_q[x][y]) x += 1 y -= 1 x = row - 1 y = y + 2 else: # 偶数，向上走 while x >= 0 and y <= column-1: z_y.append(y_q[x][y]) z_u.append(u_q[x][y]) z_v.append(v_q[x][y]) x -= 1 y += 1 x += 2 y = column - 1 # print(z_y) # print(z_u) # print(z_v)