Benchmark: rgb vs rgba vs hex canvas

HTML Preparation code:

<canvas id="canvasOne" width="400" height="400"></canvas>
<canvas id="canvasTwo" width="400" height="400"></canvas>
<canvas id="canvasThr" width="400" height="400"></canvas>

Script Preparation code:

var canvasOne = document.getElementById("canvasOne");
var ctxOne = canvasOne.getContext("2d");

var canvasTwo = document.getElementById("canvasTwo");
var ctxTwo = canvasTwo.getContext("2d");

var canvasThr = document.getElementById("canvasThr");
var ctxThr = canvasThr.getContext("2d");

Tests:

alpha
ctxOne.fillStyle = 'rgba(255, 255, 255, 0.3)'; ctxOne.fillRect(0, 0, 400, 400); ctxOne.beginPath(); ctxOne.fillStyle = 'rgba(231, 76, 60, 0.1)'; ctxOne.arc(200, 200, 100, 0, Math.PI * 2, true); ctxOne.fill();
no alpha
ctxTwo.fillStyle = 'rgba(255, 255, 255, 0.3)'; ctxTwo.fillRect(0, 0, 400, 400); ctxTwo.beginPath(); ctxTwo.fillStyle = 'rgb(249, 206, 201)'; ctxTwo.arc(200, 200, 100, 0, Math.PI * 2, true); ctxTwo.fill();
hex
ctxThr.fillStyle = 'rgba(255, 255, 255, 0.3)'; ctxThr.fillRect(0, 0, 400, 400); ctxThr.beginPath(); ctxThr.fillStyle = '#f9cec9'; ctxThr.arc(200, 200, 100, 0, Math.PI * 2, true); ctxThr.fill();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
alpha
no alpha
hex

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, compared, and their pros and cons.

**What is being tested?**

The benchmark measures the performance of different ways to set the fill style for 2D canvas drawing operations: using `rgba` with alpha channel (alpha), using `rgb` without alpha (no alpha), and using a hexadecimal color code (`#f9cec9`) as a string.

**Options compared**

The three options are:

1. **Alpha (`rgba`)**: This option uses an RGBA (Red, Green, Blue, Alpha) color model with an opacity value between 0 (fully transparent) and 1 (fully opaque). The alpha channel allows for smooth blending of colors.
2. **No alpha (`rgb`)**: This option uses an RGB (Red, Green, Blue) color model without the alpha channel, resulting in a solid fill color that may not blend well with other colors.
3. **Hexadecimal string**: This option uses a hexadecimal color code as a string to set the fill style.

**Pros and Cons of each approach**

1. **Alpha (`rgba`)**:
	* Pros: Smooth blending of colors, allows for transparent fills, compatible with most modern browsers.
	* Cons: Requires more calculations and operations on the GPU, can be slower due to the alpha channel calculation.
2. **No alpha (`rgb`)**:
	* Pros: Faster rendering, fewer calculations required, suitable for solid fill colors.
	* Cons: May not blend well with other colors, requires exact matching of RGB values, limited color range.
3. **Hexadecimal string**:
	* Pros: Fast lookup and conversion, suitable for simple fill colors, compact representation.
	* Cons: Limited color range (24-bit), may require additional calculations to convert from hex to RGBA.

**Special JS features or syntax**

The benchmark does not explicitly use any special JavaScript features or syntax that requires explanation. However, the `rgba` and `rgb` functions are part of the HTML5 canvas API and are widely supported in modern browsers.

**Other alternatives**

If the benchmark were to test other options, some potential alternatives could include:

* Using a different color model (e.g., HSL, HSV)
* Implementing custom color blending algorithms
* Testing different rendering pipelines or GPU acceleration techniques

However, these alternatives are not explicitly tested in this benchmark.

Related benchmarks:

rgb vs rgba vs hex canvas (version: 0)

Comparing performance of: alpha vs no alpha vs hex

Created: 8 years ago by: Guest

Jump to the latest result

alpha

no alpha

hex

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):