2 * Copyright 2007 Google Inc.
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
8 * http://www.apache.org/licenses/LICENSE-2.0
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
17 package com.google.zxing.common.reedsolomon;
19 import java.util.Vector;
22 * <p>Implements Reed-Solomon decoding, as the name implies.</p>
24 * <p>The algorithm will not be explained here, but the following references were helpful
25 * in creating this implementation:</p>
29 * <a href="http://www.cs.cmu.edu/afs/cs.cmu.edu/project/pscico-guyb/realworld/www/rs_decode.ps">
30 * "Decoding Reed-Solomon Codes"</a> (see discussion of Forney's Formula)</li>
31 * <li>J.I. Hall. <a href="www.mth.msu.edu/~jhall/classes/codenotes/GRS.pdf">
32 * "Chapter 5. Generalized Reed-Solomon Codes"</a>
33 * (see discussion of Euclidean algorithm)</li>
36 * <p>Much credit is due to William Rucklidge since portions of this code are an indirect
37 * port of his C++ Reed-Solomon implementation.</p>
39 * @author srowen@google.com (Sean Owen)
40 * @author William Rucklidge
42 public final class ReedSolomonDecoder {
44 private ReedSolomonDecoder() {
48 * <p>Decodes given set of received codewords, which include both data and error-correction
49 * codewords. Really, this means it uses Reed-Solomon to detect and correct errors, in-place,
52 * @param received data and error-correction codewords
53 * @param twoS number of error-correction codewords available
54 * @throws ReedSolomonException if decoding fails for any reaosn
56 public static void decode(int[] received, int twoS) throws ReedSolomonException {
57 GF256Poly poly = new GF256Poly(received);
58 int[] syndromeCoefficients = new int[twoS];
59 for (int i = 0; i < twoS; i++) {
60 syndromeCoefficients[syndromeCoefficients.length - 1 - i] = poly.evaluateAt(GF256.exp(i));
62 GF256Poly syndrome = new GF256Poly(syndromeCoefficients);
63 if (!syndrome.isZero()) { // Error
64 GF256Poly[] sigmaOmega =
65 runEuclideanAlgorithm(GF256Poly.buildMonomial(twoS, 1), syndrome, twoS);
66 int[] errorLocations = findErrorLocations(sigmaOmega[0]);
67 int[] errorMagnitudes = findErrorMagnitudes(sigmaOmega[1], errorLocations);
68 for (int i = 0; i < errorLocations.length; i++) {
69 int position = received.length - 1 - GF256.log(errorLocations[i]);
70 received[position] = GF256.addOrSubtract(received[position], errorMagnitudes[i]);
75 private static GF256Poly[] runEuclideanAlgorithm(GF256Poly a, GF256Poly b, int R)
76 throws ReedSolomonException {
77 // Assume a's degree is >= b's
78 if (a.getDegree() < b.getDegree()) {
86 GF256Poly sLast = GF256Poly.ONE;
87 GF256Poly s = GF256Poly.ZERO;
88 GF256Poly tLast = GF256Poly.ZERO;
89 GF256Poly t = GF256Poly.ONE;
91 // Run Euclidean algorithm until r's degree is less than R/2
92 while (r.getDegree() >= R / 2) {
93 GF256Poly rLastLast = rLast;
94 GF256Poly sLastLast = sLast;
95 GF256Poly tLastLast = tLast;
100 // Divide rLastLast by rLast, with quotient in q and remainder in r
101 if (rLast.isZero()) {
102 // Oops, Euclidean algorithm already terminated?
103 throw new ReedSolomonException("r_{i-1} was zero");
106 GF256Poly q = GF256Poly.ZERO;
107 int denominatorLeadingTerm = rLast.getCoefficient(rLast.getDegree());
108 int dltInverse = GF256.inverse(denominatorLeadingTerm);
109 while (r.getDegree() >= rLast.getDegree() && !r.isZero()) {
110 int degreeDiff = r.getDegree() - rLast.getDegree();
111 int scale = GF256.multiply(r.getCoefficient(r.getDegree()), dltInverse);
112 q = q.addOrSubtract(GF256Poly.buildMonomial(degreeDiff, scale));
113 r = r.addOrSubtract(rLast.multiplyByMonomial(degreeDiff, scale));
116 s = q.multiply(sLast).addOrSubtract(sLastLast);
117 t = q.multiply(tLast).addOrSubtract(tLastLast);
120 int sigmaTildeAtZero = t.getCoefficient(0);
121 if (sigmaTildeAtZero == 0) {
122 throw new ReedSolomonException("sigmaTilde(0) was zero");
125 int inverse = GF256.inverse(sigmaTildeAtZero);
126 GF256Poly sigma = t.multiply(inverse);
127 GF256Poly omega = r.multiply(inverse);
128 return new GF256Poly[]{sigma, omega};
131 private static int[] findErrorLocations(GF256Poly errorLocator)
132 throws ReedSolomonException {
133 // This is a direct application of Chien's search
134 Vector errorLocations = new Vector(3);
135 for (int i = 1; i < 256; i++) {
136 if (errorLocator.evaluateAt(i) == 0) {
137 errorLocations.addElement(new Integer(GF256.inverse(i)));
140 if (errorLocations.size() != errorLocator.getDegree()) {
141 throw new ReedSolomonException("Error locator degree does not match number of roots");
143 int[] result = new int[errorLocations.size()]; // Can't use toArray() here
144 for (int i = 0; i < result.length; i++) {
145 result[i] = ((Integer) errorLocations.elementAt(i)).intValue();
150 private static int[] findErrorMagnitudes(GF256Poly errorEvaluator,
151 int[] errorLocations) {
152 // This is directly applying Forney's Formula
153 int s = errorLocations.length;
154 int[] result = new int[s];
155 for (int i = 0; i < errorLocations.length; i++) {
156 int xiInverse = GF256.inverse(errorLocations[i]);
158 for (int j = 0; j < s; j++) {
160 denominator = GF256.multiply(denominator,
161 GF256.addOrSubtract(1, GF256.multiply(errorLocations[j], xiInverse)));
164 result[i] = GF256.multiply(errorEvaluator.evaluateAt(xiInverse),
165 GF256.inverse(denominator));