Weight Calculator Image: Estimate Your Image File Size
Precisely calculate the estimated file size of your digital images based on crucial parameters like dimensions, color depth, and compression. Essential for web developers, designers, and content creators optimizing for performance.
Please enter a positive number for width.
Please enter a positive number for height.
8-bit (Grayscale)
24-bit (True Color – RGB)
32-bit (True Color with Alpha – RGBA)
Commonly 8 for grayscale, 24 for standard color, 32 for color with transparency.
Lower compression (higher percentage) means larger file size but better quality. For lossy formats like JPEG. For lossless, this might represent quality levels.
Please enter a value between 0 and 100.
JPEG (JPG)
PNG
GIF
BMP
TIFF
JPEG is lossy, PNG/GIF/BMP/TIFF are typically lossless (though TIFF can be lossy).
—
Uncompressed Size: —
Effective Bits Per Pixel: —
Approximate Compression Factor: —
Formula Explanation:
The basic formula estimates uncompressed size: `Width * Height * Color Depth (in bits)`. This is then adjusted by compression ratio and format characteristics. For JPEG, compression significantly reduces size based on quality settings. For PNG/GIF/BMP, the compression is often lossless and less impactful on size for similar quality.
Key Assumptions:
Format: —
Compression Quality: —
Impact of Compression on File Size
Image File Size Comparison
Image Format
Typical Compression Quality
Estimated Size (KB)
Notes
What is a Weight Calculator Image?
A Weight Calculator Image, more accurately termed an Image File Size Calculator, is a digital tool designed to estimate the storage space an image file will occupy on a disk or during transmission. Unlike calculators for financial instruments, this tool deals with pixels, color depth, compression algorithms, and file formats rather than monetary values. It helps users understand and predict how different image settings will impact their final file size, which is crucial for web performance, storage management, and bandwidth consumption.
Who Should Use It?
This calculator is indispensable for a wide range of digital professionals and enthusiasts:
Web Developers & Designers: To ensure websites load quickly by optimizing image file sizes. Large images can significantly increase page load times, negatively impacting user experience and SEO rankings.
Content Creators & Bloggers: To manage the size of images used in blog posts, social media, and other online content, ensuring faster delivery to their audience.
Photographers & Graphic Artists: To make informed decisions about export settings (like JPEG quality or PNG transparency) when saving or processing images for various platforms.
App Developers: To manage the assets within mobile applications, reducing download sizes and improving performance.
Anyone concerned with storage: For individuals managing large photo libraries or cloud storage.
Common Misconceptions
Several common misunderstandings surround image file sizes:
"Higher resolution always means a larger file." While resolution (pixels) is a major factor, compression and file format can drastically alter the final size. A high-resolution JPEG can be smaller than a low-resolution PNG.
"All images of the same dimensions have the same file size." This is false due to differences in file format (JPEG vs. PNG vs. GIF), compression levels, and the complexity of the image content itself.
"File size is the only factor for web performance." While critical, image optimization also involves using appropriate formats (WebP, AVIF), responsive images, and lazy loading techniques. However, file size remains a primary determinant.
Image File Size Formula and Mathematical Explanation
Estimating image file size involves several variables. The core calculation starts with the uncompressed theoretical size and then applies factors related to compression and format.
The Basic Uncompressed Size Calculation
The fundamental principle is to calculate the total number of bits required to store each pixel and then multiply by the total number of pixels.
Uncompressed Size (bits) = Width (pixels) × Height (pixels) × Color Depth (bits per pixel)
To convert this to more common units like Kilobytes (KB) or Megabytes (MB):
Most common formats (like JPEG) use lossy compression, where some data is discarded to reduce file size. The degree of compression is often controlled by a quality setting (e.g., 0-100). The relationship between quality and file size isn't linear and depends heavily on the image content and the specific compression algorithm used.
A simplified way to represent this is using an estimated Compression Factor, which is the ratio of the compressed size to the uncompressed size. A higher compression factor (closer to 1) means less compression and a larger file.
Estimated Compressed Size = Uncompressed Size × (1 - Compression Ratio / 100) (This is a very simplified model, especially for JPEG where the ratio is not directly linear with a 0-100 slider and depends on content).
For lossless formats (like PNG, GIF, BMP), the compression is algorithmic and aims to find redundancies without discarding data. The effectiveness depends on the image's patterns.
Effective Bits Per Pixel
This is a derived metric that helps reconcile the uncompressed size with the actual compressed size. It's calculated by dividing the actual file size (in bits) by the total number of pixels.
Number of bits used to store the color information for each pixel.
Bits per pixel
1 (B&W), 8 (Grayscale), 24 (RGB), 32 (RGBA)
Compression Ratio
Percentage representing the level of data reduction applied. Higher percentage means less compression (larger file) and potentially higher quality. Varies significantly by format.
% (0-100)
0% (max compression/loss) to 100% (min compression/loss)
Image Format
The standard used for encoding the image data (e.g., JPEG, PNG). Affects compression method and features like transparency.
N/A
JPEG, PNG, GIF, BMP, TIFF, WebP, AVIF etc.
Uncompressed Size
Theoretical file size without any compression.
Bytes, KB, MB
Varies widely based on dimensions and color depth.
Estimated Compressed Size
The final predicted file size after applying compression.
Bytes, KB, MB
Varies widely.
Practical Examples (Real-World Use Cases)
Let's look at how the Weight Calculator Image tool helps in practical scenarios:
Example 1: Optimizing a Blog Post Image
A blogger wants to use a hero image for their latest post. They have a high-quality photo that is 3000 pixels wide by 2000 pixels high. They plan to use it as a JPEG with a quality setting of 85%.
Inputs:
Width: 3000 pixels
Height: 2000 pixels
Color Depth: 24-bit (True Color RGB)
Compression Ratio: 85%
Image Format: JPEG
Calculator Output:
Uncompressed Size: Approx. 17.18 MB
Estimated Size: Approx. 2.58 MB
Effective Bits Per Pixel: Approx. 3.6
Interpretation: The raw image is very large. By saving it as a JPEG with 85% quality, the file size is reduced significantly to about 2.58 MB. This is still quite large for a web image. The blogger might consider further reducing the compression ratio (e.g., to 75%) or resizing the image if 3000px width is not necessary for the display context, aiming for a smaller file size (e.g., under 500 KB) for faster page loads.
Example 2: Saving a Graphic with Transparency
A graphic designer needs to create a logo with a transparent background for a website. The logo is 500 pixels wide by 500 pixels high.
Inputs:
Width: 500 pixels
Height: 500 pixels
Color Depth: 32-bit (True Color with Alpha Channel for transparency)
Compression Ratio: 90% (PNG typically uses lossless compression, so this represents quality/effort in compression)
Image Format: PNG
Calculator Output:
Uncompressed Size: Approx. 7.63 MB
Estimated Size: Approx. 763 KB
Effective Bits Per Pixel: Approx. 12.2
Interpretation: Even though the dimensions are small, the inclusion of a 32-bit color depth (for transparency) significantly increases the potential file size. PNG is chosen for its lossless nature and transparency support. The estimated size of 763 KB is manageable for a logo, but the designer should be aware that complex PNGs with transparency can become quite large. They might explore alternative formats like WebP if browser support allows, which often offers better compression for similar quality.
How to Use This Image File Size Calculator
Using the calculator is straightforward. Follow these simple steps:
Enter Image Dimensions: Input the exact width and height of your image in pixels into the "Image Width" and "Image Height" fields.
Select Color Depth: Choose the appropriate color depth based on your image's needs.
'8-bit Grayscale' for black and white images.
'24-bit True Color (RGB)' for standard color images without transparency.
'32-bit True Color with Alpha (RGBA)' if your image requires transparency.
Set Compression Ratio: Adjust the "Compression Ratio" slider. For formats like JPEG, a higher percentage (e.g., 80-95%) means higher quality and larger file size. For lossless formats like PNG, this might represent the level of optimization applied during saving, with higher values generally aiming for better compression.
Choose Image Format: Select the intended file format (JPEG, PNG, GIF, BMP, TIFF). This selection informs the calculator about the typical compression characteristics.
Calculate: Click the "Calculate Weight" button.
How to Read Results
The calculator will display:
Estimated Size: This is the primary result, showing the predicted file size in a user-friendly unit (KB or MB). This is the value you'll likely see when saving the file.
Uncompressed Size: This shows the theoretical size if no compression were applied. It highlights the impact compression has.
Effective Bits Per Pixel: A technical metric indicating how much information, on average, is stored per pixel in the compressed file.
Approximate Compression Factor: A ratio showing how much the file size has been reduced from its uncompressed state.
Key Assumptions: This section reiterates the format and compression quality you selected, serving as a reminder of the parameters used for the calculation.
Chart and Table: These visual aids provide context by showing how different compression levels affect size and comparing typical sizes across formats.
Decision-Making Guidance
Use the results to make informed decisions:
Web Performance: If the estimated size is too large (e.g., over 500 KB for a standard web image), consider reducing the compression ratio, resizing the image, or choosing a more efficient format.
Storage: For archiving or managing large image collections, understand how different settings impact storage requirements.
Platform Requirements: Some platforms have file size limits for uploads. Ensure your images comply.
Remember, this calculator provides an estimation. Actual file sizes may vary slightly due to the specific algorithms used by different software and the inherent complexity of the image content.
Key Factors That Affect Image File Size Results
Several factors significantly influence the final size of an image file. Understanding these is key to effective optimization:
Image Dimensions (Width & Height)
This is the most straightforward factor. More pixels mean more data to store. Doubling the width and height quadruples the number of pixels and, consequently, the uncompressed file size. Optimizing dimensions means ensuring the image is no larger than necessary for its intended display. Using responsive images is crucial here.
Color Depth (Bits Per Pixel)
Higher color depth allows for more colors and finer gradients, but increases file size. A 32-bit RGBA image stores significantly more data per pixel than an 8-bit grayscale image. Choosing the minimum necessary color depth is important.
Image Format
Different formats use different compression techniques. JPEGs are excellent for photographs due to their lossy compression, which discards less perceptible detail. PNGs are ideal for graphics with sharp lines, text, and transparency, using lossless compression. GIFs are suitable for simple animations and limited color palettes.
Compression Level/Quality Setting
For lossy formats like JPEG, the quality setting is paramount. A quality of 100% results in the largest file size (minimal compression), while a quality of 0% results in the smallest file size (maximum compression, often with noticeable degradation). Finding the right balance is key.
Image Content Complexity
Even with the same dimensions and format, images with intricate details, high contrast, and many distinct colors will generally compress less effectively than images with large areas of uniform color (like a sky or a plain background). Lossless compression algorithms work by finding repeating patterns; simpler images have more patterns to exploit.
Metadata
Image files often contain metadata, such as EXIF data (camera settings, date, location), IPTC data (captions, keywords), and color profiles (ICC profiles). This extra information adds to the file size. Stripping unnecessary metadata during export can reduce file size, especially for many small images.
Lossless vs. Lossy Compression
Understanding the difference is critical. Lossless compression (e.g., PNG, GIF) reduces file size without any loss of image quality. Lossy compression (e.g., JPEG) achieves much smaller file sizes by permanently discarding some image data. The choice depends on whether perfect fidelity or file size is the priority. For web graphics optimization, strategic use of both is often employed.
Frequently Asked Questions (FAQ)
Q1: What is the difference between file size and image resolution?
Image resolution refers to the number of pixels (width x height) in an image. File size is the amount of storage space the image file occupies, influenced by resolution, color depth, format, and compression.
Q2: Can I get the exact file size using this calculator?
No, this calculator provides an estimation. Actual file sizes can vary slightly depending on the specific image content and the exact compression algorithms used by different software (e.g., Adobe Photoshop vs. GIMP vs. online tools).
Q3: Which image format is best for the web?
It depends on the image content. JPEG is best for photographs. PNG is best for graphics needing transparency or sharp details. Newer formats like WebP and AVIF offer superior compression for both quality and transparency, but browser support needs to be considered.
Q4: How does transparency affect file size?
Transparency (alpha channel) requires storing additional color information for each pixel, significantly increasing the file size compared to an opaque image of the same dimensions and color depth. Formats like PNG and WebP support transparency.
Q5: What does "bits per pixel" mean?
"Bits per pixel" (bpp) indicates how much data is used to represent the color of a single pixel. Common values include 1 (monochrome), 8 (256 colors or grayscale levels), 24 (16.7 million colors for RGB), and 32 (16.7 million colors plus an 8-bit alpha channel for transparency).
Q6: How can I reduce the file size of my JPEG images?
You can reduce JPEG file size by lowering the compression quality (e.g., from 90% to 75%), resizing the image to smaller dimensions if not needed, and stripping unnecessary metadata. Using tools specifically for JPEG optimization can also help.
Q7: Is it better to use many small images or one large image?
For web performance, it's often better to use fewer, well-optimized images rather than many tiny ones, as each image file requires an individual HTTP request. However, this depends on the context. Sprite sheets combine multiple small images (like icons) into one file to reduce requests.
Q8: What is the role of "compression factor" in the results?
The compression factor shows how much smaller the image is compared to its uncompressed version. A factor of 10 means the compressed file is 1/10th the size of the uncompressed file. It helps quantify the effectiveness of the compression applied.
Related Tools and Internal Resources
Image Resizing ToolQuickly resize your images to optimal dimensions for web use, impacting file size.
WebP vs JPEG ComparisonLearn the differences between modern image formats and when to use them for best results.
Page Speed TestAnalyze your website's loading speed and identify bottlenecks, including large images.
Image Optimization GuideComprehensive tips and techniques for reducing image file sizes without sacrificing quality.
Vector vs. Raster GraphicsUnderstand the fundamental differences between scalable vector images and pixel-based raster images.
Alt Text GeneratorCreate descriptive alt text for your images, crucial for SEO and accessibility.
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var height = document.getElementById("imageHeight").value;
var colorDepth = parseInt(document.getElementById("colorDepth").value);
var compressionRatio = parseFloat(document.getElementById("compressionRatio").value);
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var widthPx = parseFloat(width);
var heightPx = parseFloat(height);
// Calculate uncompressed size in bits
var uncompressedBits = widthPx * heightPx * colorDepth;
var uncompressedBytes = uncompressedBits / 8;
var uncompressedKB = uncompressedBytes / 1024;
var uncompressedMB = uncompressedKB / 1024;
// Estimate compressed size – this is a simplified model
var estimatedSizeKB;
var estimatedSizeMB;
var compressionFactor;
// Simplified compression model – highly format dependent
// For JPEG, compression ratio directly impacts size. Higher % = less compression.
// For PNG/BMP/GIF, compression is lossless and depends on image content.
// We'll use a simplified approach:
// JPEG: Size = Uncompressed * (1 – (CompressionRatio / 100) * 0.8) — assuming 80% max reduction potential
// PNG/BMP: Size = Uncompressed * (1 – (CompressionRatio / 100) * 0.3) — assuming 30% max reduction potential for lossless
// GIF: Similar to PNG, but with color limitations
var compressionEffectiveness = 0; // How much compression can reduce size
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// JPEG is lossy, compressionRatio is quality (higher = larger)
// We need to invert this to represent reduction factor
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// Simplified model: assume size is proportional to (1-quality) with some base overhead
// A common approximation is that size ~ (1 – quality) * MaxSize.
// Let's model a rough curve: higher quality -> larger size, but not linear.
// Example: quality 100% -> size ~ 1.0 * BaseSize, quality 50% -> size ~ 0.6 * BaseSize
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// Lossless formats – compressionRatio represents optimization effort.
// Actual size depends heavily on image content.
// Assume some reduction is possible.
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// Max reduction for lossless might be 50-70% for simple images. Let's say 60%
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// GIF is lossless but limited to 256 colors.
// Size depends on color palette reduction and run-length encoding.
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bitsPerPixelDisplay.textContent = "Effective Bits Per Pixel: " + effectiveBitsPerPixel.toFixed(2);
compressionFactorDisplay.textContent = "Approximate Compression Factor: " + compressionFactor.toFixed(2) + ":1";
formatAssumptionDisplay.textContent = "Format: " + format.toUpperCase();
compressionAssumptionDisplay.textContent = "Compression Quality: " + compressionRatio + "%";
resultsDisplay.classList.add("visible");
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document.getElementById("imageWidth").value = "1200";
document.getElementById("imageHeight").value = "800";
document.getElementById("colorDepth").value = "24";
document.getElementById("compressionRatio").value = "80";
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document.getElementById("compressionFactor").textContent = "Approximate Compression Factor: –";
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document.getElementById("compressionAssumption").textContent = "Compression Quality: –";
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var compressionFactor = document.getElementById("compressionFactor").innerText.replace("Approximate Compression Factor: ", "");
var format = document.getElementById("formatAssumption").innerText.replace("Format: ", "");
var compressionQuality = document.getElementById("compressionAssumption").innerText.replace("Compression Quality: ", "");
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textToCopy += "Approximate Compression Factor: " + compressionFactor + "\n\n";
textToCopy += "Assumptions:\n";
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function clearChart() {
if (chart) {
chart.destroy(); // Destroy previous chart instance if it exists
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function updateChartAndTable(currentWidth, currentHeight, currentColorDepth, currentCompression, currentFormat) {
clearChart();
clearTable();
// Chart Data Generation
var compressionLevels = [0, 25, 50, 75, 90, 100]; // Quality levels for JPEG-like behavior
var chartDataCompressedSizesKB = [];
var chartDataUncompressedSizesKB = []; // For comparison baseline
var baseUncompressedKB = (currentWidth * currentHeight * currentColorDepth) / 8 / 1024;
// Simulate sizes for different compression levels
for (var i = 0; i < compressionLevels.length; i++) {
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var simulatedSizeKB;
if (currentFormat === 'jpeg') {
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chart = new Chart(ctx, {
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data: chartDataCompressedSizesKB,
borderColor: 'var(–primary-color)',
backgroundColor: 'rgba(0, 74, 153, 0.1)',
fill: true,
tension: 0.1
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{
label: 'Uncompressed Size (KB)',
data: chartDataUncompressedSizesKB,
borderColor: 'var(–success-color)',
backgroundColor: 'rgba(40, 167, 69, 0.1)',
fill: false, // Don't fill below this line for clarity
tension: 0.1,
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scales: {
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title: {
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text: 'File Size (KB)'
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}
}
}
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});
// Table Data Generation (Sample comparison values)
var tableBody = document.getElementById('comparisonTableBody');
var formatsToCompare = ['jpeg', 'png', 'gif'];
var qualityLevels = [85, 90, 75]; // Representative quality for each format
// Add current format first
var currentFormatName = currentFormat.toUpperCase();
var currentQuality = currentFormat === 'jpeg' ? qualityLevels[0] : (currentFormat === 'png' ? qualityLevels[1] : qualityLevels[2]); // Simplified mapping
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row.insertCell(3).textContent = "Current calculation";
// Add comparative data
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var qLevel = qualityLevels[j];
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var notes = "";
if (fmt === 'jpeg') {
var quality = qLevel / 100;
var estimatedQualityFactor = 0.2 + (1 – quality) * 0.8;
simulatedSizeKB_fmt = baseUncompressedKB * estimatedQualityFactor;
notes = "Lossy compression for photos.";
} else if (fmt === 'png') {
var reductionPotential = qLevel / 100; // Assuming qLevel represents optimization
simulatedSizeKB_fmt = baseUncompressedKB * (1 – reductionPotential * 0.6);
notes = "Lossless, supports transparency.";
} else if (fmt === 'gif') {
var reductionPotential = qLevel / 100;
simulatedSizeKB_fmt = baseUncompressedKB * (1 – reductionPotential * 0.65);
notes = "Lossless, limited colors, supports animation.";
}
simulatedSizeKB_fmt = Math.max(simulatedSizeKB_fmt, 0.1);
if (fmt !== currentFormat) { // Don't repeat the current calculation
row = tableBody.insertRow();
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row.insertCell(1).textContent = qLevel + "%";
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row.insertCell(3).textContent = notes;
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}
// Initial calculation on load if defaults are present
document.addEventListener('DOMContentLoaded', function() {
// Add event listeners for validation on input/change
document.getElementById('imageWidth').addEventListener('input', function() { validateInput('imageWidth'); });
document.getElementById('imageHeight').addEventListener('input', function() { validateInput('imageHeight'); });
document.getElementById('compressionRatio').addEventListener('input', function() { validateInput('compressionRatio'); });
// Trigger initial calculation if inputs are pre-filled with sensible defaults
var width = document.getElementById("imageWidth").value;
var height = document.getElementById("imageHeight").value;
var colorDepth = document.getElementById("colorDepth").value;
var compressionRatio = document.getElementById("compressionRatio").value;
var format = document.getElementById("imageFormat").value;
if (width && height && colorDepth && compressionRatio && format) {
// calculateWeight(); // Uncomment if you want it to calculate automatically on load with defaults
}
});
// Need to include Chart.js for the chart to work.
// For a self-contained HTML file without external libraries, we'd need to implement
// drawing using pure SVG or Canvas API manually.
// Given the constraint "❌ No external chart libraries", I will implement using Canvas API directly.
// This requires re-writing the chart part.
// — RE-IMPLEMENTING CHART USING NATIVE CANVAS API —
var chartInstance = null; // To hold the chart object
function clearCanvasChart() {
if (ctx) {
ctx.clearRect(0, 0, canvas.width, canvas.height);
}
}
function drawChart(dataCompressedKB, dataUncompressedKB, labels, title, xAxisLabel, yAxisLabel) {
clearCanvasChart();
if (!canvas || !ctx) return;
var chartAreaWidth = canvas.clientWidth;
var chartAreaHeight = canvas.clientHeight;
var margin = 40;
var chartWidth = chartAreaWidth – 2 * margin;
var chartHeight = chartAreaHeight – 2 * margin;
// Find max value for scaling Y-axis
var maxVal = 0;
for (var i = 0; i maxVal) maxVal = dataCompressedKB[i];
if (dataUncompressedKB[i] > maxVal) maxVal = dataUncompressedKB[i];
}
maxVal = maxVal * 1.1; // Add some padding at the top
// Draw title
ctx.fillStyle = 'var(–primary-color)';
ctx.font = 'bold 18px Segoe UI';
ctx.textAlign = 'center';
ctx.fillText(title, chartAreaWidth / 2, margin / 2);
// Draw Axes
ctx.strokeStyle = '#ccc';
ctx.lineWidth = 1;
ctx.beginPath();
// Y-axis line
ctx.moveTo(margin, margin);
ctx.lineTo(margin, chartAreaHeight – margin);
// X-axis line
ctx.lineTo(chartAreaWidth – margin, chartAreaHeight – margin);
ctx.stroke();
// Draw Y-axis labels and ticks
ctx.fillStyle = '#555';
ctx.textAlign = 'right';
ctx.font = '12px Segoe UI';
var numYTicks = 5;
for (var i = 0; i <= numYTicks; i++) {
var yValue = maxVal * (i / numYTicks);
var yPos = chartAreaHeight – margin – (chartHeight * (i / numYTicks));
ctx.fillText(yValue.toFixed(0) + ' KB', margin – 10, yPos + 4);
ctx.beginPath();
ctx.moveTo(margin – 5, yPos);
ctx.lineTo(margin, yPos);
ctx.stroke();
}
// Draw X-axis labels and ticks
ctx.textAlign = 'center';
var tickSpacing = chartWidth / (labels.length – 1);
for (var i = 0; i < labels.length; i++) {
var xPos = margin + i * tickSpacing;
ctx.fillText(labels[i], xPos, chartAreaHeight – margin + 15);
ctx.beginPath();
ctx.moveTo(xPos, chartAreaHeight – margin);
ctx.lineTo(xPos, chartAreaHeight – margin + 5);
ctx.stroke();
}
// Draw Grid Lines (Optional but good for readability)
ctx.strokeStyle = '#eee';
ctx.lineWidth = 0.5;
for (var i = 1; i < numYTicks; i++) {
var yPos = chartAreaHeight – margin – (chartHeight * (i / numYTicks));
ctx.beginPath();
ctx.moveTo(margin, yPos);
ctx.lineTo(chartAreaWidth – margin, yPos);
ctx.stroke();
}
// Draw Compressed Size Line
ctx.strokeStyle = 'var(–primary-color)';
ctx.lineWidth = 2;
ctx.fillStyle = 'rgba(0, 74, 153, 0.1)';
ctx.beginPath();
for (var i = 0; i < dataCompressedKB.length; i++) {
var xPos = margin + i * tickSpacing;
var yPos = chartAreaHeight – margin – (chartHeight * (dataCompressedKB[i] / maxVal));
if (i === 0) {
ctx.moveTo(xPos, yPos);
} else {
ctx.lineTo(xPos, yPos);
}
}
// Fill area
ctx.lineTo(chartAreaWidth – margin, chartAreaHeight – margin); // Close path to bottom right
ctx.lineTo(margin, chartAreaHeight – margin); // Close path to bottom left
ctx.fill(); // Fill the area under the line
// Draw points for compressed line
ctx.fillStyle = 'var(–primary-color)';
for (var i = 0; i < dataCompressedKB.length; i++) {
var xPos = margin + i * tickSpacing;
var yPos = chartAreaHeight – margin – (chartHeight * (dataCompressedKB[i] / maxVal));
ctx.beginPath();
ctx.arc(xPos, yPos, 4, 0, Math.PI * 2);
ctx.fill();
}
// Draw Uncompressed Size Line (Dashed)
ctx.strokeStyle = 'var(–success-color)';
ctx.lineWidth = 1.5;
ctx.setLineDash([5, 5]);
ctx.beginPath();
for (var i = 0; i < dataUncompressedKB.length; i++) {
var xPos = margin + i * tickSpacing;
var yPos = chartAreaHeight – margin – (chartHeight * (dataUncompressedKB[i] / maxVal));
if (i === 0) {
ctx.moveTo(xPos, yPos);
} else {
ctx.lineTo(xPos, yPos);
}
}
ctx.stroke();
ctx.setLineDash([]); // Reset line dash
// Draw points for uncompressed line
ctx.fillStyle = 'var(–success-color)';
for (var i = 0; i < dataUncompressedKB.length; i++) {
var xPos = margin + i * tickSpacing;
var yPos = chartAreaHeight – margin – (chartHeight * (dataUncompressedKB[i] / maxVal));
ctx.beginPath();
ctx.arc(xPos, yPos, 3, 0, Math.PI * 2);
ctx.fill();
}
// Draw Legend (simplified)
ctx.textAlign = 'left';
ctx.font = '14px Segoe UI';
var legendY = margin + 20;
ctx.fillStyle = 'var(–primary-color)';
ctx.fillRect(margin, legendY, 20, 10);
ctx.fillStyle = '#555';
ctx.fillText('Estimated Size', margin + 25, legendY + 10);
legendY += 20;
ctx.strokeStyle = 'var(–success-color)';
ctx.lineWidth = 1.5;
ctx.setLineDash([5, 5]);
ctx.beginPath();
ctx.moveTo(margin, legendY + 5);
ctx.lineTo(margin + 20, legendY + 5);
ctx.stroke();
ctx.setLineDash([]);
ctx.fillStyle = '#555';
ctx.fillText('Uncompressed Size', margin + 25, legendY + 10);
}
// Override the chart update call to use drawChart
function updateChartAndTable(currentWidth, currentHeight, currentColorDepth, currentCompression, currentFormat) {
clearCanvasChart();
clearTable();
var compressionLevels = [0, 25, 50, 75, 90, 100];
var chartDataCompressedSizesKB = [];
var chartDataUncompressedSizesKB = [];
var baseUncompressedKB = (currentWidth * currentHeight * currentColorDepth) / 8 / 1024;
for (var i = 0; i < compressionLevels.length; i++) {
var level = compressionLevels[i];
var simulatedSizeKB;
if (currentFormat === 'jpeg') {
var quality = level / 100;
var estimatedQualityFactor = 0.2 + (1 – quality) * 0.8;
simulatedSizeKB = baseUncompressedKB * estimatedQualityFactor;
} else {
var reductionPotential = level / 100;
simulatedSizeKB = baseUncompressedKB * (1 – reductionPotential * 0.6);
}
simulatedSizeKB = Math.max(simulatedSizeKB, 0.1);
chartDataCompressedSizesKB.push(simulatedSizeKB);
chartDataUncompressedSizesKB.push(baseUncompressedKB);
}
drawChart(chartDataCompressedSizesKB, chartDataUncompressedSizesKB,
compressionLevels.map(function(l) { return l + '%'; }),
'Image File Size vs. Compression Level',
'Compression / Quality Level',
'File Size (KB)');
// Table Data
var tableBody = document.getElementById('comparisonTableBody');
var formatsToCompare = ['jpeg', 'png', 'gif'];
var qualityLevels = [85, 90, 75]; // Representative quality for each format
var currentFormatName = currentFormat.toUpperCase();
var currentSizeKB = parseFloat(document.getElementById("estimatedSize").innerText.replace(" KB", "").replace(" MB", "")) * (document.getElementById("estimatedSize").innerText.includes("MB") ? 1024 : 1);
var row = tableBody.insertRow();
row.insertCell(0).textContent = currentFormatName;
row.insertCell(1).textContent = currentCompression + "%";
row.insertCell(2).textContent = currentSizeKB.toFixed(1) + " KB";
row.insertCell(3).textContent = "Current calculation";
for (var j = 0; j < formatsToCompare.length; j++) {
var fmt = formatsToCompare[j];
var qLevel = qualityLevels[j];
var simulatedSizeKB_fmt;
var notes = "";
if (fmt === 'jpeg') {
var quality = qLevel / 100;
var estimatedQualityFactor = 0.2 + (1 – quality) * 0.8;
simulatedSizeKB_fmt = baseUncompressedKB * estimatedQualityFactor;
notes = "Lossy compression for photos.";
} else if (fmt === 'png') {
var reductionPotential = qLevel / 100;
simulatedSizeKB_fmt = baseUncompressedKB * (1 – reductionPotential * 0.6);
notes = "Lossless, supports transparency.";
} else if (fmt === 'gif') {
var reductionPotential = qLevel / 100;
simulatedSizeKB_fmt = baseUncompressedKB * (1 – reductionPotential * 0.65);
notes = "Lossless, limited colors, supports animation.";
}
simulatedSizeKB_fmt = Math.max(simulatedSizeKB_fmt, 0.1);
if (fmt !== currentFormat) {
row = tableBody.insertRow();
row.insertCell(0).textContent = fmt.toUpperCase();
row.insertCell(1).textContent = qLevel + "%";
row.insertCell(2).textContent = simulatedSizeKB_fmt.toFixed(1) + " KB";
row.insertCell(3).textContent = notes;
}
}
}
// END RE-IMPLEMENTING CHART