等比例压缩图片

package com.baitw.struts.utils;

import java.awt.image.BufferedImage;
import java.io.File;
import javax.imageio.ImageIO;

/**
*
* 生成等比例高质量缩略图
*
* */

public class ScaleImage {

private int width;

private int height;

private int scaleWidth;

double support = (double) 3.0;

double PI = (double) 3.14159265358978;

double[] contrib;

double[] normContrib;

double[] tmpContrib;

int startContrib, stopContrib;

int nDots;

int nHalfDots;

/**
* Start: Use Lanczos filter to replace the original algorithm for image
* scaling. Lanczos improves quality of the scaled image modify by :blade
**/

// fromFileStr原图片地址,saveToFileStr生成缩略图地址,formatWideth生成图片宽度,formatHeight高度
public void saveImageAsJpg(String fromFileStr, String saveToFileStr,
int formatWideth, int formatHeight) throws Exception {
BufferedImage srcImage;
File saveFile = new File(saveToFileStr);
File fromFile = new File(fromFileStr);
srcImage = javax.imageio.ImageIO.read(fromFile); // construct image
int imageWideth = srcImage.getWidth(null);
int imageHeight = srcImage.getHeight(null);
int changeToWideth = 0;
int changeToHeight = 0;
if (imageWideth > 0 && imageHeight > 0) {
// flag=true;
if (imageWideth / imageHeight >= formatWideth / formatHeight) {
if (imageWideth > formatWideth) {
changeToWideth = formatWideth;
changeToHeight = (imageHeight * formatWideth) / imageWideth;
} else {
changeToWideth = imageWideth;
changeToHeight = imageHeight;
}
} else {
if (imageHeight > formatHeight) {
changeToHeight = formatHeight;
changeToWideth = (imageWideth * formatHeight) / imageHeight;
} else {
changeToWideth = imageWideth;
changeToHeight = imageHeight;
}
}
}

srcImage = imageZoomOut(srcImage, changeToWideth, changeToHeight);
ImageIO.write(srcImage, "JPEG", saveFile);
}

public BufferedImage imageZoomOut(BufferedImage srcBufferImage, int w, int h) {
width = srcBufferImage.getWidth();
height = srcBufferImage.getHeight();
scaleWidth = w;

if (DetermineResultSize(w, h) == 1) {
return srcBufferImage;
}
CalContrib();
BufferedImage pbOut = HorizontalFiltering(srcBufferImage, w);
BufferedImage pbFinalOut = VerticalFiltering(pbOut, h);
return pbFinalOut;
}

//决定图像尺寸

private int DetermineResultSize(int w, int h) {
double scaleH, scaleV;
scaleH = (double) w / (double) width;
scaleV = (double) h / (double) height;
// 需要判断一下scaleH,scaleV,不做放大操作
if (scaleH >= 1.0 && scaleV >= 1.0) {
return 1;
}
return 0;

} // end of DetermineResultSize()

private double Lanczos(int i, int inWidth, int outWidth, double Support) {
double x;

x = (double) i * (double) outWidth / (double) inWidth;

return Math.sin(x * PI) / (x * PI) * Math.sin(x * PI / Support)
/ (x * PI / Support);

}

private void CalContrib() {
nHalfDots = (int) ((double) width * support / (double) scaleWidth);
nDots = nHalfDots * 2 + 1;
try {
contrib = new double[nDots];
normContrib = new double[nDots];
tmpContrib = new double[nDots];
} catch (Exception e) {
System.out.println("init contrib,normContrib,tmpContrib" + e);
}

int center = nHalfDots;
contrib[center] = 1.0;

double weight = 0.0;
int i = 0;
for (i = 1; i <= center; i++) {
contrib[center + i] = Lanczos(i, width, scaleWidth, support);
weight += contrib[center + i];
}

for (i = center - 1; i >= 0; i--) {
contrib[i] = contrib[center * 2 - i];
}

weight = weight * 2 + 1.0;

for (i = 0; i <= center; i++) {
normContrib[i] = contrib[i] / weight;
}

for (i = center + 1; i < nDots; i++) {
normContrib[i] = normContrib[center * 2 - i];
}
} // end of CalContrib()

// 处理边缘
private void CalTempContrib(int start, int stop) {
double weight = 0;

int i = 0;
for (i = start; i <= stop; i++) {
weight += contrib[i];
}

for (i = start; i <= stop; i++) {
tmpContrib[i] = contrib[i] / weight;
}

} // end of CalTempContrib()

private int GetRedValue(int rgbValue) {
int temp = rgbValue & 0x00ff0000;
return temp >> 16;
}

private int GetGreenValue(int rgbValue) {
int temp = rgbValue & 0x0000ff00;
return temp >> 8;
}

private int GetBlueValue(int rgbValue) {
return rgbValue & 0x000000ff;
}

private int ComRGB(int redValue, int greenValue, int blueValue) {

return (redValue << 16) + (greenValue << 8) + blueValue;
}

// 行水平滤波
private int HorizontalFilter(BufferedImage bufImg, int startX, int stopX,
int start, int stop, int y, double[] pContrib) {
double valueRed = 0.0;
double valueGreen = 0.0;
double valueBlue = 0.0;
int valueRGB = 0;
int i, j;

for (i = startX, j = start; i <= stopX; i++, j++) {
valueRGB = bufImg.getRGB(i, y);

valueRed += GetRedValue(valueRGB) * pContrib[j];
valueGreen += GetGreenValue(valueRGB) * pContrib[j];
valueBlue += GetBlueValue(valueRGB) * pContrib[j];
}

valueRGB = ComRGB(Clip((int) valueRed), Clip((int) valueGreen),
Clip((int) valueBlue));
return valueRGB;

} // end of HorizontalFilter()

// 图片水平滤波
private BufferedImage HorizontalFiltering(BufferedImage bufImage, int iOutW) {
int dwInW = bufImage.getWidth();
int dwInH = bufImage.getHeight();
int value = 0;
BufferedImage pbOut = new BufferedImage(iOutW, dwInH,
BufferedImage.TYPE_INT_RGB);

for (int x = 0; x < iOutW; x++) {

int startX;
int start;
int X = (int) (((double) x) * ((double) dwInW) / ((double) iOutW) + 0.5);
int y = 0;

startX = X - nHalfDots;
if (startX < 0) {
startX = 0;
start = nHalfDots - X;
} else {
start = 0;
}

int stop;
int stopX = X + nHalfDots;
if (stopX > (dwInW - 1)) {
stopX = dwInW - 1;
stop = nHalfDots + (dwInW - 1 - X);
} else {
stop = nHalfDots * 2;
}

if (start > 0 || stop < nDots - 1) {
CalTempContrib(start, stop);
for (y = 0; y < dwInH; y++) {
value = HorizontalFilter(bufImage, startX, stopX, start,
stop, y, tmpContrib);
pbOut.setRGB(x, y, value);
}
} else {
for (y = 0; y < dwInH; y++) {
value = HorizontalFilter(bufImage, startX, stopX, start,
stop, y, normContrib);
pbOut.setRGB(x, y, value);
}
}
}

return pbOut;

} // end of HorizontalFiltering()

private int VerticalFilter(BufferedImage pbInImage, int startY, int stopY,
int start, int stop, int x, double[] pContrib) {
double valueRed = 0.0;
double valueGreen = 0.0;
double valueBlue = 0.0;
int valueRGB = 0;
int i, j;

for (i = startY, j = start; i <= stopY; i++, j++) {
valueRGB = pbInImage.getRGB(x, i);

valueRed += GetRedValue(valueRGB) * pContrib[j];
valueGreen += GetGreenValue(valueRGB) * pContrib[j];
valueBlue += GetBlueValue(valueRGB) * pContrib[j];
// System.out.println(valueRed+"->"+Clip((int)valueRed)+"<-");
//
// System.out.println(valueGreen+"->"+Clip((int)valueGreen)+"<-");
// System.out.println(valueBlue+"->"+Clip((int)valueBlue)+"<-"+"-->");
}

valueRGB = ComRGB(Clip((int) valueRed), Clip((int) valueGreen),
Clip((int) valueBlue));
// System.out.println(valueRGB);
return valueRGB;

} // end of VerticalFilter()

private BufferedImage VerticalFiltering(BufferedImage pbImage, int iOutH) {
int iW = pbImage.getWidth();
int iH = pbImage.getHeight();
int value = 0;
BufferedImage pbOut = new BufferedImage(iW, iOutH,
BufferedImage.TYPE_INT_RGB);

for (int y = 0; y < iOutH; y++) {

int startY;
int start;
int Y = (int) (((double) y) * ((double) iH) / ((double) iOutH) + 0.5);

startY = Y - nHalfDots;
if (startY < 0) {
startY = 0;
start = nHalfDots - Y;
} else {
start = 0;
}

int stop;
int stopY = Y + nHalfDots;
if (stopY > (int) (iH - 1)) {
stopY = iH - 1;
stop = nHalfDots + (iH - 1 - Y);
} else {
stop = nHalfDots * 2;
}

if (start > 0 || stop < nDots - 1) {
CalTempContrib(start, stop);
for (int x = 0; x < iW; x++) {
value = VerticalFilter(pbImage, startY, stopY, start, stop,
x, tmpContrib);
pbOut.setRGB(x, y, value);
}
} else {
for (int x = 0; x < iW; x++) {
value = VerticalFilter(pbImage, startY, stopY, start, stop,
x, normContrib);
pbOut.setRGB(x, y, value);
}
}

}

return pbOut;

} // end of VerticalFiltering()

int Clip(int x) {
if (x < 0)
return 0;
if (x > 255)
return 255;
return x;
}
}

package com.baitw.struts.action;


import java.io.File;
import java.util.Date;

import org.apache.struts2.ServletActionContext;

import com.baitw.dao.ActivityDao;
import com.baitw.dao.CardTypeDao;
import com.baitw.dao.ImageDao;
import com.baitw.dao.UserDao;
import com.baitw.hibernate.entity.TActivity;
import com.opensymphony.xwork2.ActionSupport;

/**
*
* 添加活动
*
* */

public class AddActivity extends ActionSupport {


private String title;
private Date begintime;
private Date endtime;
private String content;
private Boolean state;
private Date createtime;
private Long cardTypeid;
private String username;
private ActivityDao activityDao;
private CardTypeDao cardTypeDao;
private UserDao userDao;
/*-----------------------------------------------*/
private String picture;

private File upload;
private String uploadContextType;
private String uploadFileName;
private String savePath;

private ImageDao imageDao;

private int width;
private int height;

public void setImageDao(ImageDao imageDao) {
this.imageDao = imageDao;
}

public File getUpload() {
return upload;
}

public void setUpload(File upload) {
this.upload = upload;
}

public String getUploadContextType() {
return uploadContextType;
}

public void setUploadContextType(String uploadContextType) {
this.uploadContextType = uploadContextType;
}

public String getUploadFileName() {
return uploadFileName;
}

public void setUploadFileName(String uploadFileName) {
this.uploadFileName = uploadFileName;
}

public String getSavePath() {
return ServletActionContext.getRequest().getRealPath(savePath);
}

public void setSavePath(String savePath) {
this.savePath = savePath;
}
/*-----------------------------------------------*/

public String getPicture() {
return picture;
}

public void setPicture(String picture) {
this.picture = picture;
}

public void setUserDao(UserDao userDao) {
this.userDao = userDao;
}

public void setCardTypeDao(CardTypeDao cardTypeDao) {
this.cardTypeDao = cardTypeDao;
}

public String getUsername() {
return username;
}

public void setUsername(String username) {
this.username = username;
}

public String getTitle() {
return title;
}

public void setTitle(String title) {
this.title = title;
}

public Date getBegintime() {
return begintime;
}

public void setBegintime(Date begintime) {
this.begintime = begintime;
}

public Date getEndtime() {
return endtime;
}

public void setEndtime(Date endtime) {
this.endtime = endtime;
}

public String getContent() {
return content;
}

public void setContent(String content) {
this.content = content;
}

public Boolean getState() {
return state;
}

public void setState(Boolean state) {
this.state = state;
}

public Date getCreatetime() {
return createtime;
}

public void setCreatetime(Date createtime) {
this.createtime = createtime;
}

public Long getCardTypeid() {
return cardTypeid;
}

public void setCardTypeid(Long cardTypeid) {
this.cardTypeid = cardTypeid;
}

public ActivityDao getActivityDao() {
return activityDao;
}

public void setActivityDao(ActivityDao activityDao) {
this.activityDao = activityDao;
}

@Override
public String execute() throws Exception {
// TODO Auto-generated method stub

TActivity activity=new TActivity();
activity.setTitle(title);
activity.setTCardType(cardTypeDao.getTCardType(cardTypeid.toString()));
activity.setCreatetime(createtime);
activity.setBegintime(begintime);
activity.setEndtime(endtime);
activity.setContent(content);
activity.setLogo(savePath+"/"+uploadFileName);
activity.setLogo(picture);
activity.setState(true);
activity.setTUser(userDao.getUserEntity(username));

imageDao.UploadOriginalImage(upload,
ServletActionContext.getRequest().getRealPath(savePath),
uploadFileName);

width=400;height=300;
String fileType = uploadFileName.substring(uploadFileName.lastIndexOf("."));
imageDao.formatImage(ServletActionContext.getRequest().getRealPath(savePath)
+"\\"+uploadFileName,
ServletActionContext.getRequest().getRealPath(savePath)
+"\\"+uploadFileName+"_"+width+"_"+height+fileType,
width,
height);

return activityDao.addActivity(activity);
}

public String loadAddPage() throws Exception{

return "loadAddPage";
}
}
<think>首先,用户的问题是关于在Java中进行等比例压缩图片的比例计算方法和公式。用户提供了参考站内引用,但这些都是之前的引用内容,我应该基于这些引用和自己的知识来回答。引用回顾:-[1]:提到了一个方法resizeImage,但没有给出具体的比例计算细节。-[2]:关于Spark计算框架,与图片处理无关。-[3]:关于JavaPOI操作Excel的行高和列宽比例,有点相关但不是图片压缩。-[4]:直接提到等比例缩小图片算法,并给出了一个代码片段:`newPix[newOffset+(y-newStartY)*newScansize+(x-newStartX)]=pix[offset+((int)((y-startY)*ky))*scansize+(int)((x-startX)*kx)];`,其中提到了kx和ky作为比例因子。特别地,引用[4]最直接相关,因为它涉及Java图片等比例缩小算法。引用[4]中的公式:```newPix[newOffset+(y-newStartY)*newScansize+(x-newStartX)]=pix[offset+((int)((y-startY)*ky))*scansize+(int)((x-startX)*kx)];```这里kx和ky是比例因子。在等比例压缩中,保持纵横比一致很重要。用户的查询是:"我想了解如何在Java中计算等比例压缩图片的比例",意思是"我想了解如何在Java中计算等比例压缩图片的比例"。用户具体说:"请问Java等比例压缩图片计算比例的方法和公式",所以焦点在计算比例的方法和公式上。核心概念:等比例压缩意味着要维持原始图片的宽高比(aspectratio)。所以,我们需要基于目标尺寸计算缩放比例。典型的公式:假设原始宽度为`width`,原始高度为`height`。目标可能是给定目标宽度`targetWidth`或目标高度`targetHeight`。为了保持等比例,我们需要统一比例因子。常见做法:1.如果指定目标宽度,则缩放比例`ratio=(double)targetWidth/width;`,然后新高度`newHeight=(int)(height*ratio);`2.如果指定目标高度,则`ratio=(double)targetHeight/height;`,然后新宽度`newWidth=(int)(width*ratio);`3.如果指定最大宽度或最大高度,如`maxWidth`和`maxHeight`,则可以:-计算宽度比例:`widthRatio=(double)maxWidth/width;`-计算高度比例:`heightRatio=(double)maxHeight/height;`-取较小的比例作为统一比例因子:`scalingRatio=Math.min(widthRatio,heightRatio);`-然后新宽度`newWidth=(int)(width*scalingRatio);`,新高度`newHeight=(int)(height*scalingRatio);`在引用[4]的代码中,使用`kx`和`ky`,它们是比例因子。例如,`kx=(double)originalWidth/newWidth;`或其他,但从代码看:`(int)((x-startX)*kx)`,似乎是从新坐标到旧坐标的映射。在等比例缩放中,我们需要计算出新尺寸。对于Java实现:-通常使用`BufferedImage`来获取原始图片尺寸。-然后基于目标约束计算新尺寸。-最后使用如`Image.getScaledInstance`或自定义算法如`AffineTransformOp`等来缩放图片。提供计算比例的公式:-比例因子ratio=目标尺寸/原尺寸(如对宽度或高度)。-为了等比例,缩放比例应相同。例如:-给定目标宽度:`doubleratio=targetWidth/(double)originalWidth;`-新高度=`Math.round(originalHeight*ratio);`-反之亦然。如果用户指定了同时约束宽度和高度,但想保持比例,使用取最小比例的方法。在回答中,结构要清晰:-解释基本概念。-给出公式。-提供简单Java代码示例。-根据系统指令,使用正确的LaTeX格式。系统指令:-行内数学表达式用$...$,如$x^2$。-独立公式用$$...$$并单独成段。-使用中文回答。-生成相关问题。-添加引用标识如[^1],但基于用户提供的引用,我应该引用[4],因为它最相关。用户在问题中说:"上述引用仅供参考",所以我不能直接复制引用内容,而要参考它们,并基于知识回答。生成相关问题:在回答末尾生成至少3个相关问题。回答结构:1.解释等比例压缩的概念。2.给出计算比例的公式。3.提供Java代码片段展示计算和实现。4.总结。5.生成相关问题。现在,公式:-设原始宽度为$w$,原始高度为$h$。-如果给定目标宽度$W$,则比例因子$r=\frac{W}{w}$,新高度$H=h\timesr$。-如果给定目标高度$H$,则比例因子$r=\frac{H}{h}$,新宽度$W=w\timesr$。-如果同时给最大宽度$maxW$和最大高度$maxH$,则比例因子$r=\min\left(\frac{maxW}{w},\frac{maxH}{h}\right)$,然后新尺寸为$W=w\timesr$和$H=h\timesr$。在LaTeX中:-用$r=\frac{W}{w}$等。代码示例:基于引用[4],但简化。使用BufferedImage获取尺寸,然后计算。示例代码:```javaimportjava.awt.Image;importjava.awt.image.BufferedImage;importjava.io.File;importjavax.imageio.ImageIO;publicclassImageResizer{publicstaticvoidresizeWithRatio(StringinputPath,StringoutputPath,inttargetWidth,inttargetHeight)throwsException{BufferedImageoriginalImage=ImageIO.read(newFile(inputPath));intoriginalWidth=originalImage.getWidth();intoriginalHeight=originalImage.getHeight();//计算比例因子doublewidthRatio=(double)targetWidth/originalWidth;doubleheightRatio=(double)targetHeight/originalHeight;doubleratio=Math.min(widthRatio,heightRatio);//保持等比例,取最小比例intnewWidth=(int)(originalWidth*ratio);intnewHeight=(int)(originalHeight*ratio);//缩放图片ImagescaledImage=originalImage.getScaledInstance(newWidth,newHeight,Image.SCALE_SMOOTH);BufferedImageoutputImage=newBufferedImage(newWidth,newHeight,BufferedImage.TYPE_INT_RGB);outputImage.getGraphics().drawImage(scaledImage,0,0,null);ImageIO.write(outputImage,"jpg",newFile(outputPath));//假设输出为jpg}publicstaticvoidmain(String[]args)throwsException{resizeWithRatio("input.jpg","output.jpg",800,600);//例:目标最大宽高}}```这代码展示如何计算比例并压缩。为了比例计算专门:-方法可以是:给定原始尺寸和目标约束,计算新尺寸。在回答中强调公式:$$\text{比例因子}r=\min\left(\frac{maxW}{w},\frac{maxH}{h}\right)$$其中$w$是原宽度,$h$是原高度,$maxW$是最大宽度,$maxH$是最大高度。添加引用:由于参考了引用[4],在解释时添加[^4]。相关问题:1.在Java中如何高效实现图片缩放算法?2.等比例压缩图片时如何处理不同格式(如PNGvsJPG)?3.Java图像处理库推荐?确保使用中文。</think> ### Java中计算等比例压缩图片的比例方法和公式 在Java中进行等比例压缩图片时,核心是维持图片的原始纵横比(aspect ratio),防止失真。等比例压缩意味着图片的宽高按相同比例缩放。下面我将从公式原理、计算方法到Java实现,逐步解释。 #### 1. **等比例压缩的概念和公式** 等比例压缩的关键是使用统一的比例因子(scaling ratio)来计算新尺寸。设: - 原始宽度为 $w$ (像素), - 原始高度为 $h$ (像素), - 目标宽度为 $W$ (可选约束), - 目标高度为 $H$ (可选约束)。 在实际应用中,通常有以下场景: - **场景1:指定目标宽度或高度**。例如,只给目标宽度 $W$,则比例因子 $r$ 计算为: $$ r = \frac{W}{w} $$ 新高度计算为: $$ H = h \times r $$ 类似地,如果只给目标高度 $H$,则比例因子 $r = \frac{H}{h}$,新宽度 $W = w \times r$。 - **场景2:指定最大宽度和高度(最常见)**。这用于限制图片在指定框内(如缩略图),同时保持比例。比例因子 $r$ 计算为: $$ r = \min\left( \frac{maxW}{w}, \frac{maxH}{h} \right) $$ 其中 $maxW$ 和 $maxH$ 是用户定义的最大宽度和高度。然后新尺寸计算为: $$ \text{新宽度} = w \times r, \quad \text{新高度} = h \times r $$ 这样确保图片不会超过目标框,且宽高比例一致[^4]。 公式原理:取最小比例因子能保证图片完整地适应目标范围。例如,原始图片尺寸为 $1000 \times 500$ 像素,指定 $maxW = 500$, $maxH = 400$: - 宽度比例: $\frac{500}{1000} = 0.5$ - 高度比例: $\frac{400}{500} = 0.8$ - 比例因子 $r = \min(0.5, 0.8) = 0.5$ - 新宽度:$1000 \times 0.5 = 500$ - 新高度:$500 \times 0.5 = 250$ #### 2. **Java实现步骤和代码示例** 在Java中,使用 `BufferedImage` 和 `ImageIO` 类可以轻松获取原始尺寸并计算比例。以下是完整方法: - **步骤1**: 读取原始图片,获取 $w$ 和 $h$。 - **步骤2**: 基于目标约束计算比例因子 $r$。 - **步骤3**: 计算新尺寸 $newWidth$ 和 $newHeight$。 - **步骤4**: 使用缩放方法(如 `Image.getScaledInstance()` 或 `AffineTransformOp`)压缩图片。 下面是简化代码示例,演示如何计算比例并压缩: ```java import java.awt.Image; import java.awt.image.BufferedImage; import java.io.File; import javax.imageio.ImageIO; public class ImageResizer { public static void resizeImage(String inputPath, String outputPath, int maxWidth, int maxHeight) throws Exception { // 步骤1: 读取原始图片 BufferedImage originalImage = ImageIO.read(new File(inputPath)); int originalWidth = originalImage.getWidth(); // 原始宽度 w int originalHeight = originalImage.getHeight(); // 原始高度 h // 步骤2: 计算比例因子 r double widthRatio = (double) maxWidth / originalWidth; double heightRatio = (double) maxHeight / originalHeight; double ratio = Math.min(widthRatio, heightRatio); // r = min(maxW/w, maxH/h) // 步骤3: 计算新尺寸 int newWidth = (int) (originalWidth * ratio); int newHeight = (int) (originalHeight * ratio); // 步骤4: 执行压缩(使用SMOOTH缩放算法保持质量) Image scaledImage = originalImage.getScaledInstance(newWidth, newHeight, Image.SCALE_SMOOTH); BufferedImage outputImage = new BufferedImage(newWidth, newHeight, BufferedImage.TYPE_INT_RGB); outputImage.getGraphics().drawImage(scaledImage, 0, 0, null); // 保存输出 ImageIO.write(outputImage, "jpg", new File(outputPath)); } public static void main(String[] args) throws Exception { // 示例:目标最大宽度500,最大高度400,压缩并保存 resizeImage("input.jpg", "output_resized.jpg", 500, 400); } } ``` #### 3. **注意事项** - **精度处理**: Java中比例计算结果需强制转换为 `int` 并四舍五入(如 `(int) Math.round(value)`),避免尺寸错误。 - **算法优化**: 对于大图片,建议使用高效算法如等距采样(参考引用[4])或 GPU 加速,这能减少内存占用[^4]。 - **质量控制**: 设置缩放标志(如 `Image.SCALE_SMOOTH`)确保图片清晰。JPG 格式可能损失质量,PNG 格式更适合保持细节。 - **边界情况**: 如果目标尺寸大于原始尺寸,比例因子 $r > 1$,此时图片会放大而非压缩;添加检查避免不必要放大(如 `if (r < 1)` 才压缩)。 在开发中,可复用上述公式到其他场景,如Excel尺寸调整(参考引用[3]的比例计算)或分布式图像处理框架(如Spark引用[2]),但图片压缩核心仍基于统一比例因子 $r$[^4]。 #### 4. **总结** - 等比例压缩比例计算公式为 $r = \min\left( \frac{maxW}{w}, \frac{maxH}{h} \right)$。 - Java实现简单:获取原尺寸→计算 $r$ →计算新尺寸→执行缩放。 - 推荐工具:Java内置 `BufferedImage` 或第三方库(如 Thumbnails)来简化代码。
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