RGB图像数据压缩与解压算法在单片机中的实战应用解析

文章目录

书接上回（上一篇文章已经讲解了如何封装BMP格式图片保存到本地SD卡）。

一、前言

在当今物联网(IoT)技术快速发展的背景下，嵌入式系统在各种应用场景中扮演着越来越重要的角色。随着物联网设备数量的不断增长，数据传输成为了关键的一环，尤其是在资源受限的环境中，如何高效地传输数据变得尤为重要。本文将介绍一个基于STM32F103ZET6微控制器的图像采集系统，该系统利用OV7725摄像头采集RGB565格式的图像，并通过MQTT协议将图像数据上传至阿里云物联网平台。 但是原始RGB565图像数据量巨大，直接传输会带来较高的网络负载，需要设计有效的压缩算法来减小数据体积。

考虑到单片机资源有限，无法使用第三方优秀的开源库，当前就基于Run-Length Encoding (RLE)无损压缩算法来减小图像数据的传输负担。RLE算法是一种简单有效的压缩方法，尤其适用于图像中存在大量连续重复像素值的情况。通过对重复像素的值和重复次数进行记录，可以显著减小图像数据的大小，从而降低网络传输的负载。下面会详细介绍RLE算法的设计与实现过程，以及如何在STM32F103ZET6微控制器上实现图像数据的压缩与解压，最终实现在阿里云物联网平台上进行高效的数据传输，并通过APP上位机进行图像数据的拉取与显示。

当前文章最重要的知识点是介绍： 数据的压缩和解压。

下面这个图是用的chargptAI自动生成的，不是实物图。

二、算法选型

在单片机上进行图像数据压缩，需要选择是使用轻量级、简单但有效的压缩算法。

以下2个是可以用的压缩算法，适合在单片机上实现并能够有效减小数据量。

2.1 Run-Length Encoding (RLE)

RLE 是一种非常简单的无损压缩算法，特别适用于图像中有大量连续重复像素值的情况。通过记录重复像素的值和重复次数来实现压缩。

下面是算法的核心实现： 解压和压缩

#include <stdint.h>
#include <stdlib.h>
#include <stdio.h>

// RLE 压缩函数
size_t RLE_compress(const uint16_t *input, size_t length, uint8_t *output) {
    size_t out_index = 0;
    for (size_t i = 0; i < length; ) {
        uint16_t value = input[i];
        size_t run_length = 1;
        while (i + run_length < length && input[i + run_length] == value && run_length < 255) {
            run_length++;
        }
        output[out_index++] = (uint8_t)(run_length);
        output[out_index++] = (uint8_t)(value >> 8);
        output[out_index++] = (uint8_t)(value & 0xFF);
        i += run_length;
    }
    return out_index;
}

// RLE 解压函数
size_t RLE_decompress(const uint8_t *input, size_t length, uint16_t *output) {
    size_t out_index = 0;
    for (size_t i = 0; i < length; i += 3) {
        uint8_t run_length = input[i];
        uint16_t value = (input[i + 1] << 8) | input[i + 2];
        for (size_t j = 0; j < run_length; j++) {
            output[out_index++] = value;
        }
    }
    return out_index;
}

2.2 Differential Pulse-Code Modulation (DPCM)

DPCM 适用于图像中连续像素值变化较小的情况。通过记录相邻像素值的差值来实现压缩。

下面是算法的核心实现： 解压和压缩

#include <stdint.h>
#include <stdlib.h>
#include <stdio.h>

// DPCM 压缩函数
size_t DPCM_compress(const uint16_t *input, size_t length, int16_t *output) {
    output[0] = input[0];
    for (size_t i = 1; i < length; i++) {
        output[i] = input[i] - input[i - 1];
    }
    return length;
}

// DPCM 解压函数
size_t DPCM_decompress(const int16_t *input, size_t length, uint16_t *output) {
    output[0] = input[0];
    for (size_t i = 1; i < length; i++) {
        output[i] = output[i - 1] + input[i];
    }
    return length;
}

三、采用RLE算法实现图像压缩

下面这段代码采用Run-Length Encoding (RLE) 算法对 RGB565 格式图像数据进行压缩和解压。

代码里定义了一个常量数组 gImage_rgb565_100x100，存储 100×100 图像的 RGB565 像素数据（通过实际摄像头采集得到的）。

RLE 压缩函数 RLE_compress 遍历输入的 RGB565 数据数组，查找连续相同的像素值，并记录它们的长度和值，将这些信息存储到输出的压缩数组中。每次遇到一段连续的相同值时，它会记录该段的长度（最大为 255）和该像素值的高位和低位字节。压缩后的数据长度返回给调用者。解压函数 RLE_decompress 读取压缩后的数组，根据记录的长度和值还原原始数据，将这些值存储到输出的解压数组中。main 函数中，计算图像数据的长度，然后分配内存用于存储压缩和解压后的数据，分别调用压缩和解压函数进行处理。最后，打印出原始数据大小、压缩后的数据大小以及压缩率，以验证压缩效果。所有动态分配的内存最终被释放，以避免内存泄漏。通过这样的实现，可以在不使用第三方库的情况下，有效地减少图像数据的传输负荷，提高传输效率。

实现代码：

#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>

// RLE 压缩函数
size_t RLE_compress(const uint16_t* input, size_t length, uint8_t* output) {
    size_t out_index = 0;
    for (size_t i = 0; i < length; ) {
        uint16_t value = input[i];
        size_t run_length = 1;
        while (i + run_length < length && input[i + run_length] == value && run_length < 255) {
            run_length++;
        }
        output[out_index++] = (uint8_t)(run_length);
        output[out_index++] = (uint8_t)(value >> 8);
        output[out_index++] = (uint8_t)(value & 0xFF);
        i += run_length;
    }
    return out_index;
}

// RLE 解压函数
size_t RLE_decompress(const uint8_t* input, size_t length, uint16_t* output) {
    size_t out_index = 0;
    for (size_t i = 0; i < length; i += 3) {
        uint8_t run_length = input[i];
        uint16_t value = (input[i + 1] << 8) | input[i + 2];
        for (size_t j = 0; j < run_length; j++) {
            output[out_index++] = value;
        }
    }
    return out_index;
}

int main() {
    size_t length = sizeof(gImage_rgb565_100x100) / 2;
    uint16_t* rgb565_array = (uint16_t*)gImage_rgb565_100x100;

    // 分配 RLE 压缩后的数组
    uint8_t* rle_compressed = (uint8_t*)malloc(length * 3 * sizeof(uint8_t));
    if (rle_compressed == NULL) {
        perror("Unable to allocate memory for RLE compressed array");
        return 1;
    }

    // RLE 压缩
    size_t rle_compressed_length = RLE_compress(rgb565_array, length, rle_compressed);

    // 分配 RLE 解压后的数组
    uint16_t* rle_decompressed = (uint16_t*)malloc(length * sizeof(uint16_t));
    if (rle_decompressed == NULL) {
        perror("Unable to allocate memory for RLE decompressed array");
        return 1;
    }

    // RLE 解压
    size_t rle_decompressed_length = RLE_decompress(rle_compressed, rle_compressed_length, rle_decompressed);

    // 打印 RLE 压缩率
    printf("RLE 压缩算法:\n");
    printf("原始大小: %zu bytes\n", length * 2);
    printf("压缩后大小: %zu bytes\n", rle_compressed_length);
    printf("压缩比: %.2f%%\n", (double)rle_compressed_length / (length * 2) * 100);

    // 释放内存
    free(rle_compressed);
    free(rle_decompressed);

    return 0;
}

运行效果如下：

四、哈夫曼编码实现压缩和解压

哈夫曼编码是一种常用的无损压缩算法，通过构建哈夫曼树，根据字符频率生成最优编码，从而实现数据压缩。

4.1 哈夫曼编码压缩自定义数据与还原

下面代码使用哈夫曼编码对一段自定义数据进行压缩和解压，并打印压缩前后的大小和压缩率，验证压缩效果。

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

// 定义树节点结构
typedef struct Node {
    unsigned char ch;
    unsigned int freq;
    struct Node* left, * right;
} Node;

// 优先队列
typedef struct {
    Node** nodes;
    int size;
    int capacity;
} PriorityQueue;

PriorityQueue* createPriorityQueue(int capacity) {
    PriorityQueue* pq = (PriorityQueue*)malloc(sizeof(PriorityQueue));
    pq->nodes = (Node**)malloc(capacity * sizeof(Node*));
    pq->size = 0;
    pq->capacity = capacity;
    return pq;
}

void swapNodes(Node** a, Node** b) {
    Node* t = *a;
    *a = *b;
    *b = t;
}

void heapify(PriorityQueue* pq, int idx) {
    int smallest = idx;
    int left = 2 * idx + 1;
    int right = 2 * idx + 2;
    if (left < pq->size && pq->nodes[left]->freq < pq->nodes[smallest]->freq) {
        smallest = left;
    }
    if (right < pq->size && pq->nodes[right]->freq < pq->nodes[smallest]->freq) {
        smallest = right;
    }
    if (smallest != idx) {
        swapNodes(&pq->nodes[smallest], &pq->nodes[idx]);
        heapify(pq, smallest);
    }
}

Node* extractMin(PriorityQueue* pq) {
    Node* temp = pq->nodes[0];
    pq->nodes[0] = pq->nodes[pq->size - 1];
    pq->size--;
    heapify(pq, 0);
    return temp;
}

void insertPriorityQueue(PriorityQueue* pq, Node* node) {
    pq->size++;
    int i = pq->size - 1;
    while (i && node->freq < pq->nodes[(i - 1) / 2]->freq) {
        pq->nodes[i] = pq->nodes[(i - 1) / 2];
        i = (i - 1) / 2;
    }
    pq->nodes[i] = node;
}

int isLeaf(Node* node) {
    return !(node->left) && !(node->right);
}

PriorityQueue* buildPriorityQueue(unsigned char data[], int freq[], int size) {
    PriorityQueue* pq = createPriorityQueue(size);
    for (int i = 0; i < size; ++i) {
        if (freq[data[i]] > 0) {
            Node* node = (Node*)malloc(sizeof(Node));
            node->ch = data[i];
            node->freq = freq[data[i]];
            node->left = node->right = NULL;
            pq->nodes[pq->size++] = node;
        }
    }
    for (int i = (pq->size - 1) / 2; i >= 0; --i) {
        heapify(pq, i);
    }
    return pq;
}

Node* buildHuffmanTree(unsigned char data[], int freq[], int size) {
    Node* left, * right, * top;
    PriorityQueue* pq = buildPriorityQueue(data, freq, size);
    while (pq->size != 1) {
        left = extractMin(pq);
        right = extractMin(pq);
        top = (Node*)malloc(sizeof(Node));
        top->ch = '\0';
        top->freq = left->freq + right->freq;
        top->left = left;
        top->right = right;
        insertPriorityQueue(pq, top);
    }
    return extractMin(pq);
}

void printCodes(Node* root, int arr[], int top, char** huffmanCodes) {
    if (root->left) {
        arr[top] = 0;
        printCodes(root->left, arr, top + 1, huffmanCodes);
    }
    if (root->right) {
        arr[top] = 1;
        printCodes(root->right, arr, top + 1, huffmanCodes);
    }
    if (isLeaf(root)) {
        huffmanCodes[root->ch] = (char*)malloc(top + 1);
        for (int i = 0; i < top; ++i) {
            huffmanCodes[root->ch][i] = '0' + arr[i];
        }
        huffmanCodes[root->ch][top] = '\0';
    }
}

char** buildHuffmanCodes(unsigned char data[], int freq[], int size) {
    Node* root = buildHuffmanTree(data, freq, size);
    int arr[100], top = 0;
    char** huffmanCodes = (char**)malloc(256 * sizeof(char*));
    for (int i = 0; i < 256; ++i) {
        huffmanCodes[i] = NULL;
    }
    printCodes(root, arr, top, huffmanCodes);
    return huffmanCodes;
}

void compress(unsigned char data[], int dataSize, char** huffmanCodes, unsigned char** compressedData, int* compressedSize) {
    int bitCount = 0;
    for (int i = 0; i < dataSize; ++i) {
        bitCount += strlen(huffmanCodes[data[i]]);
    }
    *compressedSize = (bitCount + 7) / 8;
    *compressedData = (unsigned char*)malloc(*compressedSize);
    memset(*compressedData, 0, *compressedSize);
    int byteIndex = 0, bitIndex = 0;
    for (int i = 0; i < dataSize; ++i) {
        char* code = huffmanCodes[data[i]];
        for (int j = 0; code[j] != '\0'; ++j) {
            if (code[j] == '1') {
                (*compressedData)[byteIndex] |= (1 << (7 - bitIndex));
            }
            bitIndex++;
            if (bitIndex == 8) {
                bitIndex = 0;
                byteIndex++;
            }
        }
    }
}

void decompress(unsigned char* compressedData, int compressedSize, Node* root, unsigned char** decompressedData, int dataSize) {
    *decompressedData = (unsigned char*)malloc(dataSize + 1);
    Node* current = root;
    int byteIndex = 0, bitIndex = 0;
    for (int i = 0; i < dataSize; ++i) {
        while (!isLeaf(current)) {
            if (compressedData[byteIndex] & (1 << (7 - bitIndex))) {
                current = current->right;
            }
            else {
                current = current->left;
            }
            bitIndex++;
            if (bitIndex == 8) {
                bitIndex = 0;
                byteIndex++;
            }
        }
        (*decompressedData)[i] = current->ch;
        current = root;
    }
    (*decompressedData)[dataSize] = '\0';
}

int main() {
    // 自定义数据
 	unsigned char data[] = "我是DS小龙哥 我正在测试这一段数据，看看能不能压缩成功";
 	int dataSize = strlen((char*)data);

    // 计算频率
    int freq[256] = { 0 };
    for (int i = 0; i < dataSize; ++i) {
        freq[data[i]]++;
    }

    // 构建哈夫曼编码
    char** huffmanCodes = buildHuffmanCodes(data, freq, 256);

    // 压缩
    unsigned char* compressedData;
    int compressedSize;
    compress(data, dataSize, huffmanCodes, &compressedData, &compressedSize);

    // 解压
    unsigned char* decompressedData;
    Node* root = buildHuffmanTree(data, freq, 256);
    decompress(compressedData, compressedSize, root, &decompressedData, dataSize);

    // 打印压缩前后的大小和压缩率
    printf("Original size: %d bytes\n", dataSize);
    printf("Compressed size: %d bytes\n", compressedSize);
    printf("Compression ratio: %.2f%%\n", (double)compressedSize / dataSize * 100);

    // 验证解压结果
    printf("Decompressed data: %s\n", decompressedData);

    // 释放内存
    free(compressedData);
    free(decompressedData);
    for (int i = 0; i < 256; ++i) {
        if (huffmanCodes[i]) {
            free(huffmanCodes[i]);
        }
    }
    free(huffmanCodes);

    return 0;
}

测试效果:

4.2 哈夫曼编码压缩完成图像的压缩和还原

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

// 定义树节点结构
typedef struct Node {
    unsigned short ch; // 使用 unsigned short 表示 RGB565 数据
    unsigned int freq;
    struct Node* left, * right;
} Node;

// 优先队列
typedef struct {
    Node** nodes;
    int size;
    int capacity;
} PriorityQueue;

PriorityQueue* createPriorityQueue(int capacity) {
    PriorityQueue* pq = (PriorityQueue*)malloc(sizeof(PriorityQueue));
    pq->nodes = (Node**)malloc(capacity * sizeof(Node*));
    pq->size = 0;
    pq->capacity = capacity;
    return pq;
}

void swapNodes(Node** a, Node** b) {
    Node* t = *a;
    *a = *b;
    *b = t;
}

void heapify(PriorityQueue* pq, int idx) {
    int smallest = idx;
    int left = 2 * idx + 1;
    int right = 2 * idx + 2;
    if (left < pq->size && pq->nodes[left]->freq < pq->nodes[smallest]->freq) {
        smallest = left;
    }
    if (right < pq->size && pq->nodes[right]->freq < pq->nodes[smallest]->freq) {
        smallest = right;
    }
    if (smallest != idx) {
        swapNodes(&pq->nodes[smallest], &pq->nodes[idx]);
        heapify(pq, smallest);
    }
}

Node* extractMin(PriorityQueue* pq) {
    Node* temp = pq->nodes[0];
    pq->nodes[0] = pq->nodes[pq->size - 1];
    pq->size--;
    heapify(pq, 0);
    return temp;
}

void insertPriorityQueue(PriorityQueue* pq, Node* node) {
    pq->size++;
    int i = pq->size - 1;
    while (i && node->freq < pq->nodes[(i - 1) / 2]->freq) {
        pq->nodes[i] = pq->nodes[(i - 1) / 2];
        i = (i - 1) / 2;
    }
    pq->nodes[i] = node;
}

int isLeaf(Node* node) {
    return !(node->left) && !(node->right);
}

PriorityQueue* buildPriorityQueue(unsigned short data[], int freq[], int size) {
    PriorityQueue* pq = createPriorityQueue(size);
    for (int i = 0; i < size; ++i) {
        if (freq[data[i]] > 0) {
            Node* node = (Node*)malloc(sizeof(Node));
            node->ch = data[i];
            node->freq = freq[data[i]];
            node->left = node->right = NULL;
            pq->nodes[pq->size++] = node;
        }
    }
    for (int i = (pq->size - 1) / 2; i >= 0; --i) {
        heapify(pq, i);
    }
    return pq;
}

Node* buildHuffmanTree(unsigned short data[], int freq[], int size) {
    Node* left, * right, * top;
    PriorityQueue* pq = buildPriorityQueue(data, freq, size);
    while (pq->size != 1) {
        left = extractMin(pq);
        right = extractMin(pq);
        top = (Node*)malloc(sizeof(Node));
        top->ch = 0;
        top->freq = left->freq + right->freq;
        top->left = left;
        top->right = right;
        insertPriorityQueue(pq, top);
    }
    return extractMin(pq);
}

void printCodes(Node* root, int arr[], int top, char** huffmanCodes) {
    if (root->left) {
        arr[top] = 0;
        printCodes(root->left, arr, top + 1, huffmanCodes);
    }
    if (root->right) {
        arr[top] = 1;
        printCodes(root->right, arr, top + 1, huffmanCodes);
    }
    if (isLeaf(root)) {
        huffmanCodes[root->ch] = (char*)malloc(top + 1);
        for (int i = 0; i < top; ++i) {
            huffmanCodes[root->ch][i] = '0' + arr[i];
        }
        huffmanCodes[root->ch][top] = '\0';
    }
}

char** buildHuffmanCodes(unsigned short data[], int freq[], int size) {
    Node* root = buildHuffmanTree(data, freq, size);
    int arr[100], top = 0;
    char** huffmanCodes = (char**)malloc(65536 * sizeof(char*)); // 65536 表示所有可能的 RGB565 值
    for (int i = 0; i < 65536; ++i) {
        huffmanCodes[i] = NULL;
    }
    printCodes(root, arr, top, huffmanCodes);
    return huffmanCodes;
}

void compress(unsigned short data[], int dataSize, char** huffmanCodes, unsigned char** compressedData, int* compressedSize) {
    int bitCount = 0;
    for (int i = 0; i < dataSize; ++i) {
        bitCount += strlen(huffmanCodes[data[i]]);
    }
    *compressedSize = (bitCount + 7) / 8;
    *compressedData = (unsigned char*)malloc(*compressedSize);
    memset(*compressedData, 0, *compressedSize);
    int byteIndex = 0, bitIndex = 0;
    for (int i = 0; i < dataSize; ++i) {
        char* code = huffmanCodes[data[i]];
        for (int j = 0; code[j] != '\0'; ++j) {
            if (code[j] == '1') {
                (*compressedData)[byteIndex] |= (1 << (7 - bitIndex));
            }
            bitIndex++;
            if (bitIndex == 8) {
                bitIndex = 0;
                byteIndex++;
            }
        }
    }
}

void decompress(unsigned char* compressedData, int compressedSize, Node* root, unsigned short** decompressedData, int dataSize) {
    *decompressedData = (unsigned short*)malloc(dataSize * sizeof(unsigned short));
    Node* current = root;
    int byteIndex = 0, bitIndex = 0;
    for (int i = 0; i < dataSize; ++i) {
        while (!isLeaf(current)) {
            if (compressedData[byteIndex] & (1 << (7 - bitIndex))) {
                current = current->right;
            }
            else {
                current = current->left;
            }
            bitIndex++;
            if (bitIndex == 8) {
                bitIndex = 0;
                byteIndex++;
            }
        }
        (*decompressedData)[i] = current->ch;
        current = root;
    }
}

int main() {
  
    int dataSize = sizeof(gImage_rgb565_100x100) / 2;
    unsigned short* data = (unsigned short*)gImage_rgb565_100x100;

    // 计算频率
    int freq[65536] = { 0 };
    for (int i = 0; i < dataSize; ++i) {
        freq[data[i]]++;
    }

    // 构建哈夫曼编码
    char** huffmanCodes = buildHuffmanCodes(data, freq, 65536);

    // 压缩
    unsigned char* compressedData;
    int compressedSize;
    compress(data, dataSize, huffmanCodes, &compressedData, &compressedSize);

    // 解压
    unsigned short* decompressedData;
    Node* root = buildHuffmanTree(data, freq, 65536);
    decompress(compressedData, compressedSize, root, &decompressedData, dataSize);

    // 打印压缩前后的大小和压缩率
    printf("Original size: %d bytes\n", dataSize * 2);
    printf("Compressed size: %d bytes\n", compressedSize);
    printf("Compression ratio: %.2f%%\n", ((double)compressedSize / (dataSize * 2)) * 100);

    // 验证解压结果（仅在开发时使用）
    for (int i = 0; i < dataSize; ++i) {
        if (data[i] != decompressedData[i]) {
            printf("Error: Decompressed data does not match original data at index %d.\n", i);
            break;
        }
    }

    // 释放内存
    free(compressedData);
    free(decompressedData);
    for (int i = 0; i < 65536; ++i) {
        if (huffmanCodes[i]) {
            free(huffmanCodes[i]);
        }
    }
    free(huffmanCodes);

    return 0;
}

作者：DS小龙哥