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How exactly does binary code work? - José Américo N L F de Freitas

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How many different "quantities" can be expressed with a binary sequence of 8 bits?

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How do you know if a binary sequence corresponds to a number, text, color, or audio?

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Which sentence best describes the way a color is represented in binary when considering the RGB system?

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How is the number 23 represented in binary?

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How can one compress a picture encoded in binary into a smaller file without loss of information?

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1. 2048, 2 4 8 16 32 64 128 256 512 1024 2048.
2. you can't
3. Hundred thousand of pixels.
4. 10111.
5. Compression is a mechanism that reduces the file size of a computer through a specific algorithm. This mechanism is a very convenient invention, especially for network users, because it can reduce the total number of bytes in the file, so that the file can be transferred faster through the slower Internet connection, in addition to reducing the disk occupation of the file space.

In signal processing, data compression, source coding,[1] or bit-rate reduction is the process of encoding information using fewer bits than the original representation.[2] Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information.[3] Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder.

The process of reducing the size of a data file is often referred to as data compression. In the context of data transmission, it is called source coding; encoding done at the source of the data before it is stored or transmitted.[4] Source coding should not be confused with channel coding, for error detection and correction or line coding, the means for mapping data onto a signal.

Compression is useful because it reduces resources required to store and transmit data. Computational resources are consumed in the compression and decompression processes. Data compression is subject to a space–time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data.[5][6]

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1 2048
2 you don't
3every colour has a binary code
4 10111
5 "Source coding" redirects here. For the term in computer programming, see Source code.

In signal processing, data compression, source coding,[1] or bit-rate reduction is the process of encoding information using fewer bits than the original representation.[2]Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information.[3] Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder.

The process of reducing the size of a data file is often referred to as data compression. In the context of data transmission, it is called source coding; encoding done at the source of the data before it is stored or transmitted.[4] Source coding should not be confused with channel coding, for error detection and correction or line coding, the means for mapping data onto a signal.

Compression is useful because it reduces resources required to store and transmit data. Computational resources are consumed in the compression and decompression processes. Data compression is subject to a space–time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data.[5][6]

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1
2048.
2
you can't.
3
every type of color has a binary code.
4.
10111.
5
Because no data is removed from the file, it can't be compressed to the same degree as lossy compression. On average, files are reduced by a maximum of 50 percent, whereas lossy compression can surpass this and provide a greater reduction in file size.

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1. 2048
2.  You can't tell very well
3.Hundreds of thousands of pixels
4. 10111
5. Reduce your page size by looking under the hood of the image to be recovered directly without any loss of precision or information to be clear, there's not a "one size fits all" format for the web ( currently ). Different types of images should be encoded into different formats

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256

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you are un able to

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the RGB  system is mainly about using 3 binary numbers to represent a colour by indicating the exact shades of the colours .

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10111

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they could use run length encoding

to change for e.g. :

blue  blue  blue  blue  blue blue blue  blue  blue  blue  to 10 blue

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1. 2048
2. You can’t be able to do it.
3. Hundreds and thousands of pixels
4. In binary the number 23 is 10111