What Is an Inline Image?

Embedded image coding refers to a binary decision sequence to identify whether an image is an "empty" image or an "all-gray" image.

Embedded image coding refers to a binary decision sequence to identify whether an image is an "empty" image or an "all-gray" image.

The two-dimensional discrete wavelet transform used in image processing is equivalent to a layered subband system, and the frequency of each subband is divided logarithmically, which represents the doubling factorization.

In a subband coding system, the coefficients of a certain subband are usually classified. The purpose of classification is to design a quantizer and an encoder. There are two typical classification methods: (1) classification based on sub-band statistics; (2) classification using wavelet transform. However, the former statistical classification will weaken the effect of outliers, that is, reduce the effect of detecting image edges.

A subband coefficient classification method based on wavelet variation was proposed by Shapiro in 1993.

A code with a very high probability of quantized zero characters is usually a low bit rate code. If it is difficult to further improve the probability of zero characters, the low bit rate coding is called effective mapping coding (mapping here refers to the quantization of wavelet coefficients to characters).

A typical transform encoder consists of three basic components: a transformer, a quantizer, and an entropy encoding.

First, the sample values of the original image are de-correlated by some lossless transformation and become a set of transformation coefficients. The transform coefficients are then quantized into a string of characters, each character corresponding to a particular quantized bit. Note that all information is actually lost at the quantization level. After that, the entropy encoding takes out a valid string, that is, a valid lossless representation of the string. Finally, the encoded

In a hierarchical octave subband system, except for the highest frequency subband, the coefficients in other frequency subbands are related to the coefficients of the higher frequency subband layer. The coefficients in the lower frequency (subband) layer are called the parent, and the coefficients in the same position in the higher frequency (subband) layer are called the child. For a given parent, the set of coefficients relative to all higher frequency (subband) layers is collectively referred to as the predecessor of that child.

Among them, the so-called isolated zero characters are those zero characters followed by valid descendant characters. They are useful zero characters for effective mapping encoding. Zero characters at the root of the zero tree are also useful. Represents useful information. Only those descendants with zero roots are invalid characters and should not be encoded.

The Shapiro coding method introduced above is often called embedded zerotree wavelet coding, and its key steps can be summarized as:

(1) Priority transfer of the position and symbol of the "effective wavelet coefficient" of the signal;

(2) Use the self-similarity of wavelet transforms between different scales to compactly encode the position of invalid wavelet coefficients;

(3) Continuous approximation of the effective wavelet coefficient amplitude.

The practical role of wavelet transform is to decorrelate the signal and concentrate all the information of the signal into a small number of wavelet coefficients with large values. These large wavelet coefficients contain much more energy than small coefficients, so that large coefficients are more important than small coefficients in signal reconstruction. Therefore, the embedded zerotree wavelet first method is to first transfer the larger (more efficient) wavelet coefficients, and then transfer the smaller wavelet coefficients. ^[1]