Compression Standard Interview Questions
Q – 1 What is vocoders and what are the types channel vocoders?
Ans- – Vocoders stands for Voice Coders.
– Synthetic sound is reproduced with artificial quality.
– Vocoders transmit signals with low bit rate, usually in the range of 1.2 to 2.4 KB.
– Model parameters are used by the receiver along with the transmitted parameters.
– Model parameters then synthesizes the approximation to the source output.
– The channel vocoders are linear predictive coders and code excited linear prediction.
Q – 2 Do you know about Digram Coding?
Ans- – It is one of the static dictionary coding forms.
– The dictionary consists all of the letters of the source alphabet.
– These letters are followed by many pair of letters. These are known as Digrams.
– Two character input is read by digram encoder.
– The dictionary is searched by the encoder for the existence of inputs.
– If input exists, the index is encoded and transmitted.
Q – 3 What is Lossless Channel?
Ans- – The lossless channel is described by a channel matrix.
– It is described with only one non-zero element in each column.
– During transmission, no source information is lost.
Q – 4 What is Progressive Transmission?
Ans- – A low resolution of an image is sent first.
– IT needs only few bits for the purpose of encoding.
– The image is then updated to the required fidelity.
– This is done by transmitting more information.
Q – 5 Explain offset in LZ77 approach?
Ans- – The sequence encoding in the look ahead buffer is encoded in this technique.
– The encoding id done by moving the encoder to a search pointer.
– The search pointer is through until a match to the first symbol is encountered.
– This symbol is available in the look ahead buffer.
– The actual distance between the pointer and the look ahead buffer is known as offset.
Q – 6 Explain two types of adaptive quantization?
Ans- 1. Forward Adaptive Quantization
2. Backward Adaptive Quantization.
Q – 7 Tell me two types of quantization errors?
Ans- 1. Granular error
2. Slope over load error.
Q – 8 Explain characteristics of a code?
Ans- – A code should be decodable.
– The code words are shorter than the letters which occur less frequently, has code word letters that occur more frequently.
Q – 9 Do you know prefix codes?
Ans- – A prefix code is a code which does not require code word as a prefix to another code word.
– Huffman code is an example for Prefix Code.
Q – 10 Explain composite source model?
Ans- – It is not simple to use a single model to describe the source in many applications.
– In these scenarios, a composite source model is used.
– Composite Source Model uses only one source.
– Only single source is activated at a given point of time.
Q – 11 Explain forward adaptive quantization?
Ans- – The source output is divided into various blocks of data.
– Every block is analyzed prior to quantization.
– As per the block analyses, the quantizer parameters are set.
– These settings are transmitted later to the receiver.
– The transmitted settings are served as side information at the receiver end.
Q – 12 Tell me about optimum prefix codes?
Ans- – Prefix coding is known as optimum coding.
– More frequently occurred symbols have shorter code words.
– Less frequently occurred symbols have longer code words.
– The less occurred frequently symbols will have equal length.
– Optimum prefix codes enhance the efficiency of data compression.
Q – 13 What do you know about companded quantization?
Ans- – Companded Quantization maps the input through compressor function.
– This function expands the probability to the high level regions.
– These regions are close to the origin.
– These regions are compresses the corresponding lower probability regions that are away from the origin.
– The output of Companded Quantization is resulted by using uniform quantizer.
– An expander function is used to transform the quantized value.
Q – 14 Explain the taxonomy of compression techniques?
Ans- – They are classified based on the requirements of reconstruction and compression of data.
– They are Lossy Compression and Lossless Compression.
Q – 15 What is Vector Quantization?
Ans- – Vector Quantization has quantizes as inputs and outputs.
– The vector quantization results in a distortion rate.
– This rate is lower than the scalar quantization.
Q – 16 What is Wavelet Based Compression?
Ans- – It is a process where a well defined temporal support for ‘wiggles’ about X-axis.
– The inner-product of the input signal is multiplied with a set of ortho-normal basis functions.
– Then the coefficients are computed by this inner-product.
Q – 17 What is Sub Band Coding?
Ans- – Sub Band Coding(SBC) is a transform coding.
– A transform code can break a signal that may result in many different ‘frequency bands’.
– Every transform code is encoded independently.
– Most of the time, it is used for compressing audio and video signals.
Q – 18 What is Arithmetic Coding?
Ans- – A variable-length entropy encoding form.
– It is used for implementing loss less data compression.
– Fewer bits are occupied when frequently used characters are represented. More bits are occupied when not-so-frequently used characters are represented.
Q – 19 What is Shannon Fano Coding?
Ans- – It is used to construct a prefix code that is based on a set of symbols.
– It suboptimal. The lowest expected code word length will not be achieved.
Q – 20 What is Huffman Coding?
Ans- – An entropy encoding algorithm.
– It uses variable length code table for encoding source symbol.
Q – 21 Do you know what is rate distortion theory?
Ans- – Distortion theory is about trade-offs between the rate and distortion.
– It is applied for compression schemes.
– An average number of bits are utilized to represent each sample value.
– If the rate of bits is decreased it is known as increase in distortion.
– If the rate of bits to represent each value is increased it is known decrease in distortion.
Q – 22 Tell me what are the parameters that are used in silence compression?
Ans- – Silence compression is used in compressing sound files.
– It is equivalent to run length coding on normal data files.
– The parameters are:
1. A threshold value. It is a parameter that specifies, below which the compression can be considered as silence.
2. A silence code followed by a single byte. It indicates the numbers of consecutive silence codes are present.
3. To specify the start of a run of silence, which is a threshold.
Q – 23 Do you know non-binary Hoffman Codes?
Ans- – The non-binary Hoffman code elements are derived from an alphabet ‘m’ is > 2 letters.
– All the symbols ‘m’ which occur least frequently will be having the same length.
– The lowest probability of the symbols ‘m’ will differ only in the last position.
– The letters that combine have code words of the same length.
– The symbols that have lowest probability will have code words with long length.
Q – 24 What is unique decipherability?
Ans- – Data symbols are encoded with coding schemes for fixed length codes.
– Every coding scheme has unique code.
– This unique encoded character ensures unambiguous.
– The encoded strings have fixed length.
– The fixed length codes are always uniquely decipherable.
Q – 25 Can you explain what is file compression and why is it necessary to compress files?
Ans- – File compression is a process to reduce the disk space to store that file.
– File compression enables data to be transferred quickly.
– Disk space needed on internet servers is reduced. This allows the servers to store more files / information with less disk space.
– File compression reduces the amount of time on internet to upload or download a file.
– Compression hides data so that not all computers can read the information stored.
– File compression is a mandatory preference for some of the internet servers to transfer files.
Q – 26 Do you know instantaneous variable length codes?
Ans- – A code that maps source symbols into a set of variable number of bits.
– A VL code compresses the sources and decompresses with zero error.
– By implementing a right coding strategy, an identically distributed source might be compressed almost close to its entropy.
– This process is in contrast to fixed length coding methods.
– Examples of variable-length codes are Huffman coding, LempelZiv code.
Q – 27 Can you explain information theory plays an important role in field of compression
Ans- – Information Theory is about quantification of information.
– It is used in compressing data.
– Entropy is a key measure of information.
– It is expressed in terms of average number of bits that are required to store a message.
– Entropy is used to quantify the uncertainty which is a process in predicting the random variable values.
– Lossless data compression, Lossy data compression and channel coding are the fundamental topics of information theory.
Q – 28 Explain what is lossless source coding?
Ans- – A data compression technique, which reverts an exact copy of original file.
– Lossless Source Coding is used for compressing text files in modems.
– Lossless Source Coding is a building block for designing lossy compressors.
– Lossy compression is implemented for images, sound and video files for effective data compression.
– Many compression techniques have a lossless mode.
– The lossless source coding involves a sequence of fixed length symbols.
– Each of these symbols is easily manipulated independently.