There are several factors that influence and distinguish compression algorithms. These factors should be carefully considered while tuning or choosing a compression algorithm for a particular usage model. Among these factors are:

  • Sensitivity to input frame types: Compression algorithms may have different compression efficiencies based on input frame characteristics, such as dynamic range, camera noise, amount of pixel to pixel correlation, resolution, and so on.
  • Target bit rate: Owing to limited bandwidth availability, some applications may need to adhere to a certain bit rate, but would sacrifice visual quality if needed.
  • Constant bit rate vs. Constant quality: Some algorithms are more suitable for transmission without buffering, as they operate with a constant bit rate. However, they may need to maintain the bit rate at the expense of visual quality for complex scenes. As video complexity varies from scene to scene, the constant bit rate requirement will result in a variable reconstruction quality. On the other hand, some algorithms maintain a somewhat constant quality throughout the video by allowing a fixed amout of distortion, or by adjusting levels of quantization based on the scene complexity. In doing so, however, they end up with a variable bit rate, which may require adequate buffering for transmission.
  • Encoder-decoder asymmetry: Some algorithms, such as vector quantization schemes, use a very complex encoder, while the decoder is implemented with a simple table look-up. Other schemes, such as the MPEG algorithms, need a higher decoder complexity compared to vector quantization, but simplify the encoder. Depending on the end-user platform, certain schemes may be more suitable than others for a particular application.
  • Complexity and implementation issues: The computational complexity, memory requirements, and openness to parallel processing are major differentiating factors for hardware or software implementation of compression algorithms. While software-based implementations are more flexible to parameter tuning for highest achievable quality and are amenable to future changes, hardware implementations are usually faster and power-optimized.
  • Error resilience: Compressed data is usually vulnerable to channel errors, but the degree of susceptibility varies from one algorithm to another. Error-correcting codes can compensate for certain errors at the cost of complexity, but often this is cost-prohitive or does not work well in case of burst errors.
  • Artifacts: Lossy compression algorithms typically produce various artifacts. The type of artifacts and its severity may vary from one algorithm to another, even at the same bit rate. Some artifacts, such as visible block boundaries, jagged edges, ringing artifacts around objects, and the like, may be visually more objectionable than random noise or a softer image. Also, the artifacts are dependent on nature of the content and the viewing condition.
  • Effect of multi-generational coding: Applications such as video editing may need multiple generations of coding and decoding, where a decoded output is used as the input to the encoder again. The output from the encoder is a second-generation compressed output. Some applications support multiple such generations of compression. Some compression algorithms are not suitable for multi-generational schemes, and often result in poor quality after the second generation of encoding the same frame.
  • System compatibility: Not all standards are available on all systems. Although one of the goals of standardization is to obtain use of common format across the industry, some vendors may emphasize one compression algorithm over another. However, compatibility with the targeted eco-system is a factor worthy of consideration when choosing a compression solution.