{"id":22175,"date":"2020-10-15T15:38:24","date_gmt":"2020-10-15T15:38:24","guid":{"rendered":"http:\/\/how_quality_and_quantity_can_go_hand_in_hand"},"modified":"2024-08-21T18:17:39","modified_gmt":"2024-08-22T01:17:39","slug":"how_quality_and_quantity_can_go_hand_in_hand","status":"publish","type":"post","link":"https:\/\/cloudinary.com\/blog\/how_quality_and_quantity_can_go_hand_in_hand","title":{"rendered":"Cloudinary’s Extended Image Quality Analysis: How Quality and Quantity Can Go Hand in Hand"},"content":{"rendered":"

When it comes to quality versus quantity, you\u2019ll often hear people say, \u201cIt\u2019s the quality that counts, not the quantity\u201d. While that\u2019s true in many situations, there are also cases where you want both quality and quantity. You may have thousands of images on your website and you want them all to look great. This is especially important if your website allows users to upload their own content, for example, to sell their own products or services. You don\u2019t want their poor quality images to reflect badly on your brand.<\/p>\n

Manually checking that each image lives up to your quality standards is incredibly time consuming, so more companies are looking towards automation as a solution. Cloudinary\u2019s extended image quality analysis<\/a> feature can help you to automate and accelerate the process of determining which images aren\u2019t up to par.<\/p>\n

Tell Me More<\/h2>\n

You can have Cloudinary analyze your existing images and\/or new images as they\u2019re being uploaded. The response to a request for quality analysis includes a variety of scores (each ranging from 0.0 to 1.0) for different aspects of the image, together with an overall weighted quality score. Take this photo as an example, shown together with its quality analysis scores:<\/p>\n

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“quality_analysis”: {
“jpeg_quality”: 0.68,
“jpeg_chroma”: 0.25,
“focus”: 0.71,
“noise”: 1.0,
“contrast”: 0.83,
“exposure”: 1.0,
“saturation”: 1.0,
“lighting”: 0.98,
“pixel_score”: 0.5,
“color_score”: 0.9,
“dct”: 0.29,
“blockiness”: 0.84,
“chroma_subsampling”: 0.15,
“resolution”: 0.92
},
“quality_score”: 0.68

<\/font><\/td><\/tr><\/table>\n

Defining Quality<\/h2>\n

Broadly speaking, the scores can be divided into pixel quality evaluations (that contribute to the pixel_score<\/code>) and color quality evaluations (that contribute to the color_score<\/code>). Images with low pixel scores look better when downscaled, so this score might not be so important to you if the display size is small compared to the overall resolution. In contrast, changing the scale of images with low color scores doesn\u2019t affect their perceived quality. Here\u2019s a quick explanation of each of the scores:<\/p>\n\"Various\n

The following scores contribute to the overall pixel_score<\/code>:<\/p>\n