Arithmetic codingArithmetic coding (AC) is a form of entropy coding used in lossless data compression. Normally, a string of characters is represented using a fixed number of bits per character, as in the ASCII code. When a string is converted to arithmetic encoding, frequently used characters will be stored with fewer bits and less-frequently occurring characters will be stored with more bits, resulting in fewer bits used in total.Data compressionIn information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information.
Context-adaptive variable-length codingContext-adaptive variable-length coding (CAVLC) is a form of entropy coding used in H.264/MPEG-4 AVC video encoding. It is an inherently lossless compression technique, like almost all entropy-coders. In H.264/MPEG-4 AVC, it is used to encode residual, zig-zag order, blocks of transform coefficients. It is an alternative to context-adaptive binary arithmetic coding (CABAC).
ProbabilityProbability concerns events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur. This number is often expressed as a percentage (%), ranging from 0% to 100%. A simple example is the tossing of a fair (unbiased) coin.Lossless compressionLossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).Bit rateIn telecommunications and computing, bit rate (bitrate or as a variable R) is the number of bits that are conveyed or processed per unit of time. The bit rate is expressed as bits per second (symbol: bit/s), often with an SI prefix such as kilo (1 kbit/s = 1,000 bit/s), mega (1 Mbit/s = 1,000 kbit/s), giga (1 Gbit/s = 1,000 Mbit/s) or tera (1 Tbit/s = 1,000 Gbit/s). The non-standard abbreviation bps is often used: 1 Mbps is 1 Mbit/s, that is, one million bits per second.
High Efficiency Video CodingVideo compression format, succeeds H.264/MPEG-4 AVCHigh Efficiency Video Coding (HEVC), also known as H.265 and MPEG-H Part 2, is a patent-encumbered video compression standard designed as part of the MPEG-H project as a successor to the widely used Advanced Video Coding (AVC, H.264, or MPEG-4 Part 10). The standard was published in 2013.
Advanced Video CodingWidely used standard for video compressionBlock diagram of video coding layer of H.264 encoder with perceptual quality scoreAdvanced Video Coding (AVC), also referred to as H.264 or MPEG-4 Part 10, is a video compression standard based on block-oriented, motion-compensated coding. It is by far the most commonly used format for the recording, compression, and distribution of video content, used by 79% of video industry developers as of December 2024.
Quantization (signal processing)In mathematics and digital signal processing, quantization is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding.Entropy codingIn information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source.
Lossy compressionIn information technology, lossy compression, or irreversible compression, is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. Higher degrees of approximation create coarser images as more details are removed.*As an Amazon Associate I earn from qualifying purchases.