Benchmark: image from blob vs native image src with decode 2

Tests:

a
(async () => { const blob = await (await fetch('https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png')).blob(); const img = new Image(); img.src = URL.createObjectURL(blob); return await img.decode(); })();
b
(async () => { const img = new Image(); img.src = 'https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png'; return await img.decode(); })();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

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Test case name	Result
a
b

Fastest: N/A

Slowest: N/A

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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I'd be happy to explain the benchmark and its various components.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmarking test case on the MeasureThat.net website. The test aims to compare two approaches for loading and displaying an image in a web page:

1. Loading the image from a blob (binary large object) using the `fetch` API.
2. Using a native image source URL directly.

**Options Compared**

The benchmark compares the performance of these two approaches:

* **Blob approach**: Loads the image data as a binary blob and creates an `Image` object to display it. The blob is created by fetching the image URL via the `fetch` API.
* **Native image src approach**: Directly sets the `src` attribute of an `Image` object to the native image source URL.

**Pros and Cons**

* **Blob approach:**
	+ Pros:
		- Can handle binary data with a higher degree of control over its format and encoding.
		- May be useful for displaying images in environments where native image sources are not supported or have limitations.
	+ Cons:
		- Requires additional steps to create the blob, which can introduce overhead.
		- May require more memory to store the blob data.
* **Native image src approach:**
	+ Pros:
		- Faster and more lightweight, as it only sets the `src` attribute of an existing `Image` object.
	+ Cons:
		- Limited control over the image's format, encoding, or display characteristics.

**Libraries Used**

In this benchmark, no libraries are explicitly mentioned. However, the use of `fetch` API and `URL.createObjectURL()` suggests that modern JavaScript environments support these built-in APIs without requiring additional library imports.

**Special JS Features/Syntax**

The benchmark uses the following special features:

* **Async/await syntax**: The test cases use async functions with await expressions to handle asynchronous operations.
* **Template literals**: The `Image` object creation and blob generation code uses template literals for string interpolation.
* **Object URL creation**: The `URL.createObjectURL()` method is used to create a unique URL for the blob.

**Other Alternatives**

If you were to reimplement this benchmark using alternative approaches, consider the following options:

* Using Web Workers: Load the image data in a separate thread (Web Worker) and then display it in the main thread.
* Using Canvas or SVG elements: Display the image using HTML5 canvas or SVG elements instead of an `Image` object.
* Using Image loading libraries like ImageLoader.js: Utilize specialized libraries designed for efficient image loading and management.

Keep in mind that these alternatives might introduce new complexities, dependencies, or performance trade-offs compared to the original blob and native image src approaches.

Related benchmarks:

image from blob vs native image src with decode 2 (version: 1)

image from blob vs native image src with decode 2

Comparing performance of: a vs b

Created: 3 years ago by: Registered User

Jump to the latest result

a

b

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):