Hybrid vector image quantization
Vector image quantization allows the maximum efficiency to be achieved for block image encoding. A shortcoming of an optimal vector quantization block (OVQB) is the presence of a large number of operations consisting of squaring, adding, or subtracting (see [1–3]). Using a block for vector quantization with tree encoding (BVQTE), the number of operations can be reduced (see [4-6]), but the required memory capacity is increased. When OVQB and BVQTE are combined, one can obtain a hybrid vector quantization (HVQB) which allows a high efficiency to be ensured without the shortcomings inherent in the quantization processes in the case when OVQB and BVQTE are used separately.
The authors have developed a principle of operation and an optimization algorithm for HVQB which has been presented in the present work along with the theoretical results of optimizing certain of its modifications for vector image quantization. Then the quantization efficiency, number of operations, and requirements governing the memory capacity of the device are estimated, and a comparison is made with results obtained for independent use of OVQB and BVQTE.
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