## 3 Act
### Compression  
#### 3.3.1 Compression Technique  
The ACT uses three types of compression techniques, namely, DWT, ADPCM, and RLC. DWT and RLC are usually used in image compression such as JPEG2000, and ADPCM is used in audio recording [36-40]. Wavelet transform exploits both the spatial and frequency correlation of data by dilations and translations of mother wavelet on the input data. It supports the multi resolution analysis of data, i.e., it can be applied to different scales according to the details required, which allows progressive transmission and zooming of the data without the need of extra storage. Another encouraging feature of wavelet transform is its symmetric nature that is both the forward and the inverse transform has the same complexity, building fast compression and decompression routines. The Discrete Wavelet Transform (DWT), which is based on sub-band coding is found to yield a fast computation of the Wavelet Transform. It is easy to implement and reduces the computation time and resources required. A time-scale representation of the digital signal is obtained using digital filtering techniques. The signal to be analyzed is passed through filters with different cutoff frequencies at different scales. The DWT is computed by successive low-pass and high-pass filtering of the discrete time-domain signal. This is called the Mallat algorithm or Mallat-tree decomposition. Its significance is in the manner it connects the continuous-time multi resolution to discrete-time filters. At each level, the high pass filter produces detail information, while the low pass filter associated with scaling function produces coarse approximations. The Wavelet Transform at high frequencies gives good time resolution and poor frequency resolution, while at low frequency the Wavelet Transform gives good frequency resolution and poor time resolution. Its characteristics well suited for data compression include the ability to take into account of very good energy compaction capabilities, robustness under transmission, high compression ratio etc. Wavelet transform divides the information of data into approximation and detail sub-signals. The approximation sub-signal shows the general trend of data values and other three detail sub-signals show the vertical, horizontal and diagonal details or changes in the data. Adaptive DPCM (ADPCM) is a variant of Differential Pulse-Code Modulation (DPCM) that varies the size of the quantization step, to allow further reduction of the required bandwidth for a given signal-to-noise ratio. DPCM or differential pulse-code modulation is a signal encoder that uses the baseline of PCM but adds some functionalities based on the prediction of the samples of the signal. The input can be an analog signal or a digital signal. A quantizer is used to reduce the number of bits needed to store the transformed coefficients by reducing the precision of those values. As it is a many-to-one mapping, it is a lossy process and is the main source of compression in an encoder. Quantizationcan be performed on each individual coefficient, which is called Scalar Quantization (SQ). Quantization can also be applied on a group of coefficients together known as Vector Quantization (VQ). Both uniform and non-uniform quantizers can be used depending on the problems. An entropy encoder supplementarily compresses the quantized values losslessly to provide a better overall compression. It uses a model to perfectly determine the probabilities for each quantized value and produces an appropriate code based on these probabilities so that the resultant output code stream is smaller than the input stream. The most commonly used entropy encoders are the Huffman encoder and the arithmetic encoder, although for applications requiring fast execution, simple Run Length Coding (RLC) is very effective. It is important to note that a properly designed quantizer and entropy encoder are absolutely necessary along with optimum signal transformation to get the best possible compression.