Analysis of Classification and Conversion of Image Compression Technologies

I. Lossless Compression Technology: "Precise Compression" with Complete Image Quality Retained

Lossless compression achieves compression by identifying and eliminating structural redundancies in images (such as repeated pixels and regular color blocks). After decompression, it can fully restore every pixel information of the original image, and is suitable for scenarios with extremely high requirements for image quality precision.

Core Algorithm Principles

LZW Algorithm: Establishes a mapping table of "repeated pixel sequences - short codes" and replaces repeatedly occurring pixel combinations (e.g., continuous blue sky pixels) with short codes. It is commonly used in GIF and TIFF formats.

Huffman Coding: Allocates code lengths based on the frequency of pixel values (short codes for high-frequency values and long codes for low-frequency values) to reduce the overall data volume. It is widely used in PNG and PDF image compression.

DEFLATE Algorithm: Combines the advantages of LZW and Huffman coding. It first compresses repeated sequences through LZW, then optimizes the symbol length with Huffman coding. It is the core algorithm for PNG and ZIP image compression.

(II) Lossy Compression Technology: Sacrificing Details for Efficient Compression Lossy compression

utilizes the visual characteristics of the human eye (sensitivity to brightness, insensitivity to chromaticity, and insensitivity to high-frequency details) to actively discard information that is imperceptible to the human eye (such as subtle color differences and blurred edge details) in exchange for a higher compression ratio. It is suitable for scenarios where moderate image quality is required but volume control is necessary.

Core Algorithm Principles

DCT Transform (Discrete Cosine Transform):

Divides the image into 8×8 pixel blocks, converts spatial domain data into frequency domain through mathematical transformation (low frequencies correspond to the overall outline, high frequencies correspond to details), retains low frequencies and discards high frequencies. It is the core algorithm of JPEG.

Wavelet Transform: More flexible than DCT, it can process image details of different scales simultaneously, compressing redundancies while retaining key information. It is used in JPEG 2000 and WebP (partial modes).

Block-based Coding: Divides the image into macroblocks and eliminates redundancies through motion estimation (between video frames) or texture prediction (static images). New-generation formats such as HEIF and AVIF adopt this type of algorithm.

II. Conversion of Image Compression:

Adaptation Logic of Formats and Technologies The "conversion" in image compression is not only a change of format, but also a process of selecting compression technologies according to scenario requirements. Its core goal is to balance the relationship among volume, image quality and compatibility.

(I) Core Types of Conversion Mutual Conversion Between Lossless and Lossy

Lossless → Lossy: Convert lossless formats such as PNG and TIFF to JPEG and WebP (lossy mode). The compression ratio is controlled by adjusting quality parameters (such as JPEG's "quality factor" 0-100), which is suitable for generating web - used images from high-precision originals.

Lossy → Lossless: Converting JPEG to PNG can only retain the current image quality (cannot restore lost details), and the file volume will increase. It is only used for special needs such as requiring a transparent channel.

Format Conversion Within the Same Type Lossless Internal Conversion:

For example, converting PNG to GIF (needs to reduce colors to 256) and TIFF to PNG (reduces volume while retaining transparency), which is used to adapt to the format requirements of different platforms.

Lossy Internal Conversion:

For example, converting JPEG to WebP (reduces volume by 30% under the same image quality) and JPEG to HEIF (reduces volume by half) are used to improve compression efficiency. It is necessary to consider the compatibility of the target platform (for example, if an old browser does not support WebP, it is necessary to fallback to JPEG).

Adjustment of Compression Parameters The volume can be changed by modifying compression parameters in the same format. For example, increasing the quality factor of JPEG (from 70 to 90) will increase the volume but improve the image quality; WebP adjusts the "lossless" parameter to switch between lossless and lossy modes.

(II) Conversion Tools and Precautions Common Tools:

Professional Software: Photoshop (supports batch conversion of multiple formats and can finely adjust parameters), GIMP (open - source, suitable for lossless conversion);

Online Tools: Squoosh (launched by Google, supports real - time preview of compression effects), TinyPNG (focuses on PNG/JPEG compression);

Command - line Tools: ImageMagick (for batch processing, suitable for automated scripts), cwebp (official WebP conversion tool).

Precautions: Avoid multiple "lossy to lossy" conversions:

Each lossy compression will accumulate image quality loss (for example, converting JPEG to WebP and then to JPEG will cause serious loss of details);

Prioritize new - generation formats: When compatibility permits, use WebP and HEIF instead of JPEG to balance volume and image quality;

Keep the original: After conversion, the original lossless file should be saved for subsequent secondary editing.