/**
 * @license
 * Copyright 2021 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
/**
 * An image quantizer that improves on the speed of a standard K-Means algorithm
 * by implementing several optimizations, including deduping identical pixels
 * and a triangle inequality rule that reduces the number of comparisons needed
 * to identify which cluster a point should be moved to.
 *
 * Wsmeans stands for Weighted Square Means.
 *
 * This algorithm was designed by M. Emre Celebi, and was found in their 2011
 * paper, Improving the Performance of K-Means for Color Quantization.
 * https://arxiv.org/abs/1101.0395
 */
export declare class QuantizerWsmeans {
    /**
     * @param inputPixels Colors in ARGB format.
     * @param startingClusters Defines the initial state of the quantizer. Passing
     *     an empty array is fine, the implementation will create its own initial
     *     state that leads to reproducible results for the same inputs.
     *     Passing an array that is the result of Wu quantization leads to higher
     *     quality results.
     * @param maxColors The number of colors to divide the image into. A lower
     *     number of colors may be returned.
     * @return Colors in ARGB format.
     */
    static quantize(inputPixels: number[], startingClusters: number[], maxColors: number): Map<number, number>;
}