ICT 9626 - Graphics Creation

Image Compression

Image compression is the process of reducing the amount of data required to represent an image. This is crucial for efficient storage and transmission of images over networks. Image compression techniques can be broadly classified into two categories: lossless and lossy.

Lossless Compression

Lossless compression techniques reduce file size without losing any of the original image data. The original image can be perfectly reconstructed from the compressed file. This is essential for images where data integrity is paramount, such as medical images or text-based graphics.

Common Lossless Compression Methods:

• Run-Length Encoding (RLE): This method is effective for images with long sequences of identical pixels. It replaces these sequences with a single value and the number of repetitions.
• Huffman Coding: This is a variable-length coding scheme that assigns shorter codes to more frequent pixel values and longer codes to less frequent ones.
• Lempel-Ziv (LZ77, LZ78): These are dictionary-based compression algorithms that identify repeating patterns in the image data and replace them with references to the dictionary.

Advantages of Lossless Compression:

• No data loss: The original image is perfectly reconstructed.
• Suitable for images where data integrity is critical.

Disadvantages of Lossless Compression:

• Generally achieves lower compression ratios compared to lossy compression.

Lossy Compression

Lossy compression techniques achieve higher compression ratios by discarding some of the original image data. This results in a smaller file size but with a potential loss of image quality. The level of data loss can be controlled, allowing users to trade off file size and image quality.

Common Lossy Compression Methods:

• Discrete Cosine Transform (DCT): This is the foundation of JPEG compression. The image is divided into small blocks, and the DCT transforms these blocks into frequency components. High-frequency components, which represent fine details, are often discarded.
• Quantization: This process reduces the number of possible values for the frequency components, further reducing file size. Quantization introduces some error, which is the source of data loss.
• Chroma Subsampling: This technique exploits the fact that the human eye is less sensitive to changes in color than in brightness. It reduces the resolution of the color components (chroma) while keeping the brightness component (luma) at full resolution.

Advantages of Lossy Compression:

• Achieves significantly higher compression ratios compared to lossless compression.
• Suitable for photographs and images where some loss of quality is acceptable.

Disadvantages of Lossy Compression:

• Data loss: The original image cannot be perfectly reconstructed.
• Repeated compression and decompression can lead to a gradual degradation of image quality.

Comparison Table

Feature	Lossless Compression	Lossy Compression
Data Loss	No	Yes
Compression Ratio	Lower	Higher
Image Quality	Original	Reduced
Suitable for	Medical images, text graphics	Photographs, web graphics