Image Compression Example redundant Information theory lossless lossy data information data redundancy

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Mervyn Neal
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1 Image Compression Images usually require an enormous amount of data to represent: Example: A standard 8.5 by sheet of paper scanned at samples per inch (dpi) and quantized to two gray levels (binary image) would require more than k bytes to represent. ( 8.5 )( ) / bytes. Image compression involves reducing the amount of data (bits) required to represent a digital image. This is done by removing redundant information. Image compression is crucial for efficient storage, retrieval, transmission, and manipulation of images. More generally, data compression involves the efficient representation of data in digital format. Information theory --- a field pioneered by Claude E. Shannon in the 94s --- is the theoretical basis for most data compression techniques. Compression can be either lossless (information preserving) or lossy. In lossy compression, one can achieve higher levels of data reduction with less than perfect reproduction of the original image. Data compression refers to the process of reducing the amount of data required to represent a given quantity of information. Various amounts of data may be used to represent/describe the same information. Some representations maybe less efficient in the sense that they have data redundancy.

2 If n and n 2 are the number of information carrying units (ex. bits) in two datasets that represent the same information, the relative redundancy R D of the first dataset is defined as RD, where CR C R n n 2 is called the compression ratio. For images, data redundancy can be of three types: Coding redundancy: This refers to the binary codewords used to represent grayvalues. Interpixel redundancy: This refers to the correlation between adjacent pixels in an image. Psychovisual redundancy: This refers to the unequal sensitivity of the human eye to different visual information. A fidelity criterion or error criterion is required to quantify the loss of information (if any) due to a compression scheme. Objective fidelity criteria are based on some quantitive function of the original input image and the compressed and subsequently decompressed image. Example: Root-mean-square (RMS) error, which is defined as the square-root of the MSE. e( m, fˆ( m, f ( m, f ( m, : Original image f ˆ( m, : Reconstructed image e ( m, : Error image

3 e M N 2 [ fˆ( m, f ( m, n ] rms ) MN m n A related measure is the mean-square signal-to-noise ratio (SNR ms ): SNR ms [ fˆ( m, ] M N [ fˆ( m, f ( m, ] m n M N m n The rms value of SNR, denoted SNR rms, is the square-root of SNR rms. When the ultimate image is to be viewed by a human, subjective fidelity criterion may be more appropriate. Here, image quality is measured by subjective evaluations by a human observer. Ratings by a number of human observers, based on typical decompressed images, are averaged to obtain this subjective fidelity criterion

4 Example of an absolute comparison scale: Value Rating Description Excellent An image of extremely high quality --- as good as desired. 2 Fine An image of high quality, providing enjoyable viewing. Interference is not objectionable. 3 Passable An image of acceptable quality. Interference is not objectionable. 4 Marginal An image of poor quality; you wish you could improve it. Interference is somewhat objectionable. 5 Inferior A very poor image, but you could watch it. Objectionable interference is definitely present. 6 Unusable An image so bad that you cold not watch it. Example of a relative comparison scale, based on a side-by-side comparison of the original image and the decompressed image: Value Rating Much Worse Worse Slightly Worse Same Slightly better Better Much Better

5 Image Compression model f ( m, Source Encoder Channel Encoder Channel Channel Decoder Source Decoder fˆ ( m, Source Encoder is used to remove redundancy in the input image. Channel Encoder is used to introduce redundancy in a controlled fashion to help combat noise. Example: Parity bit. This provides a certain level of immunity from noise that is inherent in any storage/transmission system. If the channel is not prone to noise, this block maybe eliminated. The Channel could be a communication link or a storage/retrieval system. Channel Decoder and Source Decoder invert the operations of the corresponding encoder blocks. We will mainly concentrate on the source encoder/decoder blocks and not on the channel encoder/decoder steps.

6 Source Encoder and Decoder Source encoder is responsible for reducing or eliminating any coding, interpixel, or psychovisual redundancy. f ( m, Mapper Quantizer Source Encoder Symbol Encoder To Channel (Compressed Image) The first block Mapper transforms the input data into a (usually nonvisual) format, designed to reduce interpixel redundancy. This block is reversible and may or may not reduce the amount of data. Example: run-length encoding, image transform. The Quantizer reduces accuracy of the mapper output in accordance with some fidelity criterion. This block reduces psychovisual redundancy and is usually not invertible. The Symbol Encoder creates a fixed or variable length codeword to represent the quantizer output and maps the output in accordance with this code. This block is reversible and reduces coding redundancy. From Channel (Compressed Image) Symbol Decoder Inverse Mapper fˆ ( m, Source Decoder The decoder blocks are inverse operations of the corresponding encoder blocks (except the quantizer block, which is not invertible).

7 Elements of Information Theory What is information --- how to quantify it? What is the minimum amount of data that is sufficient to represent an image without loss of information? What is theoretically the best compression possible? What is the theoretically best possible transmission rate for reliable communication over a noisy channel? Information theory provides answers to these and other related fundamental questions. The fundamental premise of information theory is that the generation of information can be modeled as a probabilistic process. A discrete source of information generates one of N possible symbols from a source alphabet set A { a, a,, an }, in unit time. Example: A { a, b, c,, z}, {, }, {,, 2,, 255}. The source output can be modeled as a discrete random variable E, which can take values in set A { a, a,, an }, with p p,. corresponding probabilities { },, p N We will denote the symbol probabilities by the vector T z a ), P( a ), P( a ) p, p p. [ ] [ ] T P( N, N N Naturally, p, and. i p i i The information source is characterized by the pair ( A,z).

8 Observing an occurrence (or realizatio of the random variable E results in some gain of information denoted by I(E). This gain of information was defined to be (Shanno: I ( E) log log P( E) P( E) The base for the logarithm depends on the units for measuring information. Usually, we use base 2, which gives the information in units of binary digits or bits. Using a base logarithm would give the entropy in the units of decimal digits. The amount of information attributed to an event E is inversely related to the probability of that event. Examples: Certain event: P ( E).. In this case I ( E) log(/). This agrees with intuition, since if the event E is certain to occur (has probability ), knowing that it has occurred has not led to any gain of information. Coin toss: P ( E Heads). 5. In this case I( E) log(/.5) log(2) bit. This again agrees with intuition. Rare event: P ( E).. In this case I( E) log(/.) log() 9.97 bits. This again agrees with intuition, since knowing that a rare event has occurred leads to a significant gain of information. The entropy H(z) of a source is defined as the average amount of information gained by observing a single source symbol: H ( z) N i p i log p i

9 By convention, in the above formula, we set log. The entropy of a source quantifies the randomness of a source. Higher the source entropy, more the uncertainty associated with a source output, and higher the information associated with a source. For a fixed number of source symbols, the entropy is maximized if all the symbols are equally likely (recall uniform histogram). Example: Symbol ai Probability p i ½ ¼ 2 2 /8 3 3 /6 4 4 / / /64 6 Information (in bits) I ( a i ) log p i Source entropy: 6 H ( z) p i log p i i [ 2log 2 + 4log log 64] [ ] bits 32

10 Given that a source produces the above symbols with indicated probabilities, how do we represent them using binary strings? Symbol ai Probability Binary String Length of (Codeword) codeword l i ½ 3 ¼ 3 2 /8 3 3 /6 3 4 / / /64 3 Average length of a codeword: L pili pi (3) 3 pi i i i 3 bits Is this is the best we can do (in terms of L )? For a fixed length codeword scheme, yes. How about if we employ a variable length scheme? Idea: Since the symbols are not all equally likely, assign shorter codewords to symbols with higher probability and longer codewords to symbols with lower probability, such that the average length is smaller.

11 Consider the following scheme Symbol ai Probability p i Binary String (Codeword) ½ ¼ 2 2 /8 3 3 /6 4 4 / / /64 6 L 6 p l i i 32 i Length of codeword l i [ ] bit Notice that this is the same as the source entropy! Shannon s noiseless coding theorem Let (A,z) be a discrete source with probability vector z and entropy H(z). The average codeword length of any distortionless (uniquely decodable) coding is bounded by In other words, no codes exist that can losslessly represent the source if L < H( z). L H ( z)

12 Note that Shannon s theorem is quite general in that it refers to any code, not a particular coding scheme. Also, it does not specify a scheme to construct codes whose average length satisfies L H( z), nor does it claim that a code satisfying L H ( z) exists. Indeed, the Huffman code (to be studied later) is a particular algorithm for assigning codewords, with average codeword length satisfying H ( z) L < H ( z) + Using a block coding scheme (code a block of n symbols at a time instead of a single symbol), one can obtain codewords with average codeword length satisfying L H ( z) L n < H ( z) + n The efficiency of an encoding scheme is defined as H ( z) η L

Reading.. IMAGE COMPRESSION- I IMAGE COMPRESSION. Image compression. Data Redundancy. Lossy vs Lossless Compression. Chapter 8.

Reading.. IMAGE COMPRESSION- I Week VIII Feb 25 Chapter 8 Sections 8.1, 8.2 8.3 (selected topics) 8.4 (Huffman, run-length, loss-less predictive) 8.5 (lossy predictive, transform coding basics) 8.6 Image