Google's New Compression is More Efficient than JPEGs
References: arxiv.org & thenextweb
Researchers for Google at Cornell University have created a compression algorithm that sounds as though it's straight out of HBO's comedy series 'Silicon Valley.' The compression algorithm, which was announced in a recently released paper, uses a neural network and a "middle-out" system to compress images more effectively than the standard JPEG compression rate.
To understand the new compression algorithm, it's first essential to understand neural networks like Google's open source 'TensorFlow.' Neural networks are artificial intelligence systems that function in a similar way to the human brain, learning from patterns based on the input they receive.
For the new compression algorithm, the researchers fed TensorFlow six million images and selected the 100 least effective compression sections in each of these. It then had TensorFlow compress these difficult sections, giving it a sort of trial by fire. The outcome was a more efficient compression algorithm.
To understand the new compression algorithm, it's first essential to understand neural networks like Google's open source 'TensorFlow.' Neural networks are artificial intelligence systems that function in a similar way to the human brain, learning from patterns based on the input they receive.
For the new compression algorithm, the researchers fed TensorFlow six million images and selected the 100 least effective compression sections in each of these. It then had TensorFlow compress these difficult sections, giving it a sort of trial by fire. The outcome was a more efficient compression algorithm.
Trend Themes
1. Neural Network Compression - Google's compression algorithm uses neural networks to achieve more efficient image compression than JPEGs.
2. Middle-out Compression - The 'middle-out' system used in Google's compression algorithm offers disruptive innovation potential for more effective image compression techniques.
3. Machine Learning-enabled Compression - The integration of machine learning, such as with Google's TensorFlow, in compression algorithms presents opportunities for improved image compression.
Industry Implications
1. Artificial Intelligence - The advancements in neural network compression offer transformative potential for the artificial intelligence industry.
2. Image Processing - The development of more efficient compression algorithms provides valuable innovation opportunities for the image processing industry.
3. Data Management - Incorporating machine learning into compression algorithms presents opportunities for enhanced data management practices across various industries.
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