Export Quality

[Rule Uresti]

Iam trying to use Figma as my main software to produce my designs.

But my frames are being exported with a bad resolution from Figma.

Exporting as PNG and even using 2X is not getting the full resolution as I see on Figma.

I am trying to export a png file from Designer, but unfortunately I am not getting the image quality to be what I expected. I am trying to create a sharp logo which can be used on website, social media etc.

I have kept the default settings before exporting this. However after exporting this the image just doesn't look as sharp and crisp as expected. I have attempted this with JPEG export also and ensured that the quality setting is set to 'Best Quality'. I have attached a screenshot of the export and also the resulting files in both PNG and JPEG. I have also attached a PDF version to compare.

To get a clean png file I had to export it at 1200dpi. I did this from Illustrator. I then tried from Designer, I started with my document size and did not change the DPI, it was default set to 300. I placed the file and then went back and changed to 1200dpi, results were not go and did not match my Illustrator export with same document specs. I went back to Designer and started off correct this time with document size (which was the same as before) and changed from 300 DPI to 1200 DPI. This gave me results matching Illustrator, as it should. I find if you have to have text and convert to png, jpeg or any other like format you really need to boost the DPI to get clean results.

As @Pšenda indicates when exporting to PDF or SVG the text will most likely remain as vector, unless an effect has been applied. When previewed and zoom in it will look crisp, exporting to other formats such as JPEG or PNG will rasterise the text on export.

Can you confirm that you have setup your document size correctly so when exported and viewed at 100% this is the size you want to display it at. I would like to make sure as your project is 600x100px (pixels) this is the same as 5.08x0.85cm (centimetres). This is 100% and the size you should be viewing the file at and not zooming in at all, as this will distort the image.

I've attached a screenshot, I've set my document up in Designer at 600x100px inserted some text and then exported to PNG. The two previews below are the PNG file opened and viewed at 100%, the left is 300DPI the right is 96DPI.

I can confirm I have I have not zoomed in and I am viewing the file in its actual size (i.e. 100%). I have attached screenshot of how I have setup the document and the steps followed. Please see attached.

As long as you are exporting to JPEG or PNG (both raster image formats) at 600x100 px (which is the size including the white space around the rasterized text) & at any reasonable DPI, it won't matter much what you try -- there are just not going to be enough pixels in the rasterized image to display every letter crisply.

@Ali1 Your original issue has to do with attempting to export a 600x100 px asset "as is" from a vector package. In my experience there is just NO chance to get a good quality acceptable result, even in Illustrator. This is caused by a couple of things, such as non-decimal positioning, which adds unwanted soft anti-aliasing to the edges.

Of course, if you are already working at a @3x or @4x multiplier, the above steps generally are not required, because your base resolution is already very high. But it depends a bit on the image editor: the only way to check for quality is to go though the above steps at least one time, and compare your manually exported version with the automatic ones.

@Lee DThe problem that Affinity Designer can not export sharp and especially smooth text to PNG, known for more than a year. Even free Inkscape exports significantly better PNG. It is simply incomprehensible why Affinity development team does not improve this. It is a basic requirement that PNG files are sharp and smooth!

I wonder if SketchUp still has issues with Antialiasing on the Mac. You could also try switching Antialiasin off for your image exports, export to a very high resolution, and downsample the resulting image in your image editor.

This is a weird resolution and fps - probably variable. But anyway, if output quality is the only issue try upping the Quality to 60% (or higher, like 65-70%; never use 100%!). Keep in mind some quality will be lost simply by exporting the video so you need to decide if you want smaller filesize with a bit less quality or huge files.

The quality of goods produced and exported by a country affects its economic environment in different ways. The literature often highlights the role played by demand for product quality in foreign markets as a decisive incentive for firms to improve product quality to be competitive in the export market.Footnote 1 Firms that choose to upgrade the quality of their export products need to become more intensive in skilled labor. The studies of Verhoogen (2008) and Brambilla et al. (2012) examine and verify the positive association between exporting to the US and the wages offered by firms in the cases of Mexico and Argentina. Their results imply that firms exporting to high-income markets need to upgrade the quality of export products and, for this reason, to increase the number of skilled workers employed. This circumstance generates a demand for skilled employees, which translates into an increase in average wages paid by firms.

This paper is oriented within this context and intends to provide insights into the quality provision mechanism according to which production of high-quality products requires the employment of skilled labor, which, in turn, demands higher wages. With this view, our analysis aims at examining the importance of quality in explaining the wage premium. The study is conducted on the manufacturing sector over a set of 23 countries and 118 different industries and makes use of data on the average level of wages paid at the industry level for each country. The use of data at such a large scale does not allow us to distinguish the documented effect across firms within the same industry. However, thanks to the broad coverage of our dataset, we are able to provide worldwide evidence of the quality provision mechanism.

We find a positive and significant causal relationship between quality upgrades of export products and wage premia at the industry level. Our results indicate that an industry that upgrades the quality of its export products by moving from the first quartile to the second quartile of the quality distribution shows an increase in its wage premia of 3.5%.Footnote 2 Moreover, we find the effect of product quality upgrades on wage premia to be stronger for low-income countries. This is likely to be explained by the fact that in low-income countries, the wage differential between low-skilled and high-skilled workers is larger. This finding suggests that a rise in demand for skilled labor due to export product quality upgrades could have beneficial effects on economic development, especially in low-income countries where there is low incentive to invest in education.

This paper is closely related to the work of Brambilla and Porto (2016), which studies the importance of exporting to countries where quality is valued more and establishes a link between destination-country income and average wages paid at the industry level on a large panel of countries. In their study, they use exported product prices as an indicator of quality. However, relying on prices alone poses limitations, as the price disparity between two varieties could stem from factors other than quality differences, such as lower production costs, undervalued exchange rates, or pricing-to-market strategies. With respect to Brambilla and Porto (2016) we provide evidence of the quality provision mechanism using a measure of quality of exported products based on a structural estimation derived from our theoretical model. This approach offers the advantage of providing a more robust and accurate assessment of quality, as it takes into account various factors that influence quality other than prices, such as market conditions, production capabilities, and trade dynamics.

This paper contributes to the literature on the relationship between export product quality and labor force composition. The seminal paper of Verhoogen (2008) examines wages offered by Mexican firms that increased their exports after the peso devaluation in 1995 and finds that firms exporting to the US are more skill-intensive and pay, on average, higher wages to both skilled and unskilled workers. Using Argentinian data, Brambilla et al. (2012) show that Argentinian firms that increase exports to richer countries are characterized by higher skill composition of their workforce and, therefore, pay on average higher wages. Similarly, the study of Brambilla et al. (2019) explores the link between firm export status and demand for skilled tasks and finds that exporters use more skilled labor than non-exporters.

Our study is also related to the literature on export specialization and country development. The seminal paper of Hausmann et al. (2007) points out that certain products are growth-promoting and that countries that produce and export these kinds of products are likely to grow faster. The ability of developing countries to specialize in the production and export of products that permit knowledge and technological spillovers is seen as a determinant of long-run economic growth (Hausmann et al., 2014). While several studies analyze the dynamics of industry specialization (Schott, 2003; Chiappini, 2014), inter-industry mobility (Ban, 2017) and the role of growth-promoting products in relation to country income trends, our analysis focuses on the upgrading of product quality as a potential determinant of wage increases.Footnote 3

The remainder of this paper is organized as follows. In Sect. 2, we present the theoretical model describing the relationship between export quality and wages. In Sect. 3, we present the data used in the analysis and the methodology used to obtain the measure of quality of exported products at the industry level. In Sect. 4, we present the identification strategy and the main results. In Sect. 5, we present some robustness analysis, and Sect. 6 concludes.

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