page_title: Video quality
section_title: Classification of objective video quality models
hierarchical_section_title: Video quality / Objective video quality / Classification of objective video quality models
caption_attribution_description: English: An overview of NR image and video quality assessment methods. The second row of boxes gives a division into three main categories, further divided into subcategories in the next row. The bottom row gives examples of extracted features or information used for processing in each subcategory.
context_page_description: Video quality is a characteristic of a video passed through a video transmission/processing system, a formal or informal measure of perceived video degradation. Video processing systems may introduce some amount of distortion or artifacts in the video signal, which negatively impacts the user's perception of a system. For many stakeholders such as content providers, service providers, and network operators, the assurance of video quality is an important task.
  Video quality evaluation is performed to describe the quality of a set of video sequences under study. Video quality can be evaluated objectively or subjectively. Also, the quality of a system can be determined offline, or in-service.
context_section_description: Objective models can be classified by the amount of information available about the original signal, the received signal, or whether there is a signal present at all: 
  Full Reference Methods (FR): FR models compute the quality difference by comparing the original video signal against the received video signal. Typically, every pixel from the source is compared against the corresponding pixel at the received video, with no knowledge about the encoding or transmission process in between. More elaborate algorithms may choose to combine the pixel-based estimation with other approaches such as described below. FR models are usually the most accurate at the expense of higher computational effort. As they require availability of the original video before transmission or coding, they cannot be used in all situations (e.g., where the quality is measured from a client device).
  Reduced Reference Methods (RR): RR models extract some features of both videos and compare them to give a quality score. They are used when all the original video is not available, or when it would be practically impossible to do so, e.g. in a transmission with a limited bandwidth.  This makes them more efficient than FR models at the expense of lower accuracy.
  No-Reference Methods (NR): NR models try to assess the quality of a distorted video without any reference to the original signal. Due to the absence of an original signal, they may be less accurate than FR or RR approaches, but are more efficient to compute.
  Pixel-Based Methods (NR-P): Pixel-based models use a decoded representation of the signal and analyze the quality based on the pixel information. Some of these evaluate specific degradation types only, such as blurring or other coding artifacts.
  Parametric/Bitstream Methods (NR-B): These models make use of features extracted from the transmission container and/or video bitstream, e.g. MPEG-TS packet headers, motion vectors and quantization parameters. They do not have access to the original signal and require no decoding of the video, which makes them more efficient. In contrast to NR-P models, they have no access to the final decoded signal. However, the picture quality predictions they deliver are not very accurate.
  Hybrid Methods (Hybrid NR-P-B): Hybrid models combine parameters extracted from the bitstream with a decoded video signal. They are therefore a mix between NR-P and NR-B models.
caption: No-reference image and video quality assessment methods.
exif: {}