How do you evaluate the quality and accuracy of compressed data?

1 Lossless vs lossy compression

Compression algorithms can be classified into two categories: lossless and lossy. Lossless compression preserves the exact information of the original data, while lossy compression discards some information to achieve higher compression ratios. Lossless compression is suitable for data that requires high fidelity, such as text, code, or encryption. Lossy compression is more suitable for data that can tolerate some distortion, such as images, audio, or video.

2 Compression ratio

One of the most basic metrics for evaluating the quality and accuracy of compressed data is the compression ratio, which is the ratio of the size of the compressed data to the size of the original data. The higher the compression ratio, the more space is saved by compression. However, a high compression ratio does not necessarily mean a high quality or accuracy, as it may also imply a high loss of information. Therefore, compression ratio should be considered along with other metrics that measure the quality and accuracy of the compressed data.

3 Bit error rate

Bit error rate (BER) is a metric that measures the number of bits that are changed or corrupted during compression and decompression. BER is usually expressed as a percentage or a fraction of the total number of bits in the data. A low BER indicates a high accuracy of the compressed data, as it means that the compression algorithm preserves most of the original bits. A high BER indicates a low accuracy of the compressed data, as it means that the compression algorithm introduces many errors or distortions. BER is more relevant for lossless compression than for lossy compression, as lossy compression intentionally alters some bits to achieve higher compression ratios.

4 Peak signal-to-noise ratio

Peak signal-to-noise ratio (PSNR) is a metric that measures the quality of the compressed data in terms of the ratio of the maximum possible signal power to the noise power. PSNR is usually expressed in decibels (dB) and is commonly used for image and video compression. A high PSNR indicates a high quality of the compressed data, as it means that the compression algorithm preserves most of the signal and minimizes the noise. A low PSNR indicates a low quality of the compressed data, as it means that the compression algorithm introduces a lot of noise or distortion. PSNR is more relevant for lossy compression than for lossless compression, as lossless compression does not introduce any noise or distortion.

5 Structural similarity index

Structural similarity index (SSIM) is a metric that measures the quality of the compressed data in terms of the similarity of the structure, contrast, and luminance of the original and compressed data. SSIM is usually expressed as a value between 0 and 1, where 1 means perfect similarity and 0 means no similarity. SSIM is also commonly used for image and video compression, and it is more sensitive to perceptual aspects of quality than PSNR. A high SSIM indicates a high quality of the compressed data, as it means that the compression algorithm preserves most of the structural features of the original data. A low SSIM indicates a low quality of the compressed data, as it means that the compression algorithm alters or degrades the structural features of the original data.

6 Subjective evaluation

Besides the objective metrics mentioned above, another way to evaluate the quality and accuracy of compressed data is to use subjective evaluation, which involves human judgment and feedback. Subjective evaluation can be conducted by asking users or experts to rate or compare the quality and accuracy of the original and compressed data, using scales or criteria such as preference, satisfaction, or acceptability. Subjective evaluation can capture the nuances and preferences of different users and applications, and it can complement the objective metrics by providing more insights and context. However, subjective evaluation can also be influenced by various factors, such as bias, expectations, or environment, and it can be more costly and time-consuming than objective evaluation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?