Distortion control issues in image compression

Image compression is a commonly used technical method when storing and transmitting images. It can reduce the storage space of images and speed up the transmission of images. The goal of image compression is to reduce the size of the image file as much as possible while trying to maintain the visual quality of the image so that it can be accepted by the human eye. However, during the image compression process, a certain degree of distortion often occurs. This article discusses the issue of distortion control in image compression and provides some concrete code examples.

1. JPEG compression algorithm and its distortion issues
   JPEG is a common image compression standard that uses a compression algorithm based on the discrete cosine transform (DCT). The core of the JPEG compression algorithm is to divide the image into several 8×8 small blocks, perform DCT transformation on each small block, and quantize and encode the coefficients. However, distortion is introduced during the quantization process, resulting in reduced image quality.

The following is a simple JPEG compression code example:

import numpy as np
import cv2

def jpeg_compression(image, quality):
    # 将图像分成若干个8×8的小块
    height, width, _ = image.shape
    blocks = []
    for i in range(height // 8):
        for j in range(width // 8):
            block = image[i*8:(i+1)*8, j*8:(j+1)*8, :]
            blocks.append(block)

    # 对每个小块进行DCT变换，并进行量化和编码
    compressed_blocks = []
    for block in blocks:
        # 进行DCT变换
        dct_block = cv2.dct(block.astype(np.float32))

        # 进行量化和编码
        quantized_block = np.round(dct_block / quality)
        compressed_blocks.append(quantized_block)

    # 将压缩后的小块重组成图像
    compressed_image = np.zeros_like(image)
    for i in range(height // 8):
        for j in range(width // 8):
            block = compressed_blocks[i*(width//8)+j]
            compressed_image[i*8:(i+1)*8, j*8:(j+1)*8, :] = cv2.idct(block)

    return compressed_image.astype(np.uint8)

In the above code, the quality parameter represents the compression quality, ranging from 1 to 100 , the smaller the value, the lower the compression quality and the greater the distortion.

2. Control of compression quality and distortion
   There is a certain trade-off between compression quality and image distortion. In practical applications, according to different needs, the compression quality parameters can be adjusted to control the degree of distortion.

In addition, in order to reduce the distortion introduced by image compression, some enhancement algorithms can also be used. For example, in the JPEG compression algorithm, a perception-based quantization table can be used to control distortion, and the image can be converted into a color space before DCT transformation, which can improve the compression effect, etc.

3. Distortion control issues of other image compression algorithms
   In addition to the JPEG algorithm, there are some other image compression algorithms, such as PNG, GIF, etc. They each have different characteristics and distortion issues. For example, the PNG compression algorithm is based on lossless compression, which does not introduce visible distortion, but cannot compress very small; while the GIF compression algorithm is based on indexed color, which can cause color distortion.

To sum up, the issue of distortion control in image compression is an issue that needs attention. In practical applications, we need to select appropriate compression algorithms and parameters according to specific needs to achieve the required image quality and compression ratio. At the same time, by using enhancement algorithms, such as adjusting quantization tables, color space conversion, etc., the compression effect can be improved to a certain extent.

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