Quantization techniques pdf

Detection of perturbed quantization (PQ) steganography based on empirical matrix

Perturbed Quantization (PQ) steganography scheme is almost undetectable with the current steganalysis methods. We present a new steganalysis method for detection of this data hiding algorithm. We show that the PQ method distorts the dependencies of DCT coefficient values; especially changes much lower than significant bit planes. For steganalysis of PQ, we propose features extraction from the e...

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Facilitating the acquisition of small targets is an active area of research. Despite the usefulness of the techniques proposed so far, we show why this topic can benefit from more fundamental work. We investigate three factors that might account for the observed drop in performance when selecting small targets: motor accuracy, visual legibility and quantization. Our findings suggest that visual...

Minimum mean square error quantizers with uncorrelated input and quantization noise

Scalar quantizers with the minimum mean square error are designed in the case where the input signal and the quantization noise are uncorrelated. The probability density function (pdf) of the stochastic signal is assumed to be known. This design is then generalized to vector quantizers in the case where the expected value of the inner product between the input and quantization noise vector are ...

Quantization and coding pdf

Rate-Distortion Bounds for High-Resolution Vector Quantization via Gibbs's Inequality

Gibbs’s inequality states that the differential entropy of a random variable with probability density function (pdf) f is less than or equal to its cross entropy with any other pdf g defined on the same alphabet, i.e., h(X) ≤ −E[log g(X)]. Using this inequality with a cleverly chosen g, we derive a lower bound on the smallest output entropy that can be achieved by quantizing a d-dimensional sou...