Image Compression. OUTLINE: Exploiting coding redundancy, interpixel redundancy, and psychovisual redundancy. Lossy and lossless methods. (viii) In digital image compression, three basic data redundancies can be identified and exploited: Coding redundancy, Inter-pixel redundancy. Next: Binary image compression Up: compression Previous: The algorithm. Inter- pixel Redundancy and Compression. Subsections. Binary image compression.

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Lossy compression is most commonly used to compress multimedia data audio, video, still imagesespecially in applications such as streaming media and internet telephony. How to measure data redundancy? Follow by Huffman coding. To use this website, you must agree to our Privacy Policyincluding cookie policy.

Registration Forgot your password? An example of mapping can be to map pixels of an image: Image compression methods based on the elimination of psychovisually redundant data usually called quantization are usually applied to commercial broadcast TV and similar applications for human visualization.

The key component is the predictor, whose function is to generate an estimated predicted value for each pixel from the input image based on previous pixel values. Because of its fast lookup capabilities at the decoder side, VQ-based coding schemes are particularly attractive to multimedia applications.

Value Range 0 black white. There are several local, global, and adaptive prediction algorithms in the literature. That means neighboring pixels are not statistically independent.

Educational and business documents, medical images CT, MRI and digital radiologymotion pictures, satellite images, weather maps, geological surveys, Hide my email completely instead? Removing a large amount of redundancy leads to efficient video compression. The Psychovisual redundancies exist because human perception does not involve quantitative analysis of every pixel or luminance value in the image.


Image Compression – Fundamentals and Lossless Compression Techniques – ppt video online download

The information is represented in the form of codes. Separately encode DC components and transmit data. My presentations Profile Feedback Log out. The key factor behind the success of transform-based coding schemes many of the resulting coefficients for most natural images have small magnitudes and can be quantized or discarded altogether without causing significant distortion in the decoded image.

The quantization stage is at the core of any lossy image encoding algorithm. Certain information simply has less relative importance than other information in normal visual processing In general, an observer searches for distinguishing features such as edges or textural regions and mentally combines them into recognizable groupings.

Published by Dana Hudson Modified over 3 years ago. Transform coding techniques use a reversible, linear mathematical reedundancy to map the pixel values onto a set of coefficients, which are then quantized and encoded.

It is described in more detail in a separate short article. In order to do so, the original 2-D array of pixels is usually mapped into a different format, e. Because wavelet transforms are capable redundanvy representing an input signal with multiple levels of resolution, and yet maintain the useful compaction properties of the DCTthe subdivision of the input image into smaller subimages is no longer necessary.

Coding redundancy, Inter-pixel redundancy and Psychovisual redundancy. These techniques must find an appropriate balance between the amount of error loss and the resulting bit savings. Interpixel redundancy is due to the correlation between the neighboring pixels in an image. Wavelet coding techniques are also based on the idea that the coefficients of a transform that decorrelates the pixels of an image can be coded more efficiently than the original pixels themselves.

This redundancy can be explored in several ways, one of which is by predicting a pixel value based on the values of its neighboring pixels. Since prediction errors are likely to be smaller than the original pixel values, redundanxy VLC encoder will likely generate shorter codewords.


The way each of them is explored is briefly described below. The gray levels are not equally probable. Auth with social network: The concept of entropy is mathematically described in equation 1: Assign shorter code words to the gray levels that occur most frequently or vice versa.

Digital Image Processing Image Compression – ppt video online download

Interpixel temporal redundancy is the statistical correlation between pixels from successive frames in video sequence. Download our mobile app and study on-the-go.

Auth with social network: Paste the link into your website, email, or any other HTML document. This operation is reversible. At some later time, the compressed image is decompressed to reconstruct the original image information preserving or lossless techniques or an approximation of it lossy techniques. As a consequence of this narrower distribution — and consequently reduced entropy — Huffman coding or other VLC schemes will produce shorter codewords for the difference image.

Digital Image Processing Image Compression

Arrange the resulting coefficients in a zigzag order. If the output intervals are equally spaced, which is the reundancy way to do it, the process is called uniform scalar quantization; otherwise, for reasons usually related to minimization of total distortion, it is called nonuniform scalar quantization. Registration Forgot your password?

In most cases, a variable-length code is used. Engineering in your pocket Download our mobile app and study on-the-go.