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# How does Transform Coding Works?

**Transform coding **is a *lossy compression technique *that is widely used in digital signal processing, data compression, and image and audio compression. It works by converting the time-domain data into a frequency-domain representation, where the energy of the signal is concentrated in fewer coefficients. These coefficients are then quantized and encoded, resulting in a compressed version of the signal.

The following is a detailed explanation of **how transform coding works**:

1. **Transform:** The first step in transform coding is to transform the time-domain signal into a frequency-domain representation. The most commonly used transform is the Discrete Fourier Transform (DFT), which transforms a time-domain signal into its frequency components. Other transforms like the Discrete Cosine Transform (DCT), Wavelet Transform, and Karhunen-Loeve Transform (KLT) can also be used depending on the application.

2. **Quantization: **Once the signal is transformed into the frequency domain, the next step is to quantize the coefficients. Quantization involves rounding off the coefficients to a smaller number of bits, which reduces the precision of the coefficients. This process results in a loss of information, which is why transform coding is a lossy compression technique.

3. **Encoding: **After quantization, the coefficients are encoded using various coding techniques like Huffman coding, arithmetic coding, or entropy coding. The objective of encoding is to reduce the number of bits required to represent the coefficients, thereby achieving compression.

4. **Decoding:** The decoding process involves the reverse of the encoding process, i.e., the encoded coefficients are decoded to obtain the quantized coefficients. The decoded coefficients are then used to reconstruct the frequency-domain signal.

5. **Inverse Transform: **Finally, the inverse transform is applied to the reconstructed signal to obtain the time-domain representation of the compressed signal.

Transform coding is widely used in various applications, including audio and image compression, video compression, and data compression. The choice of transform and quantization parameters depends on the application and the desired level of compression.

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