Human eyes are unable to respond with equal sensitivity to all visual input. Psycho visual redundancy is based on human vision's features. This is accomplished by allocating fewer bits to the additional grey scale values than to the less feasible bits. Huffman coding can help to minimise duplication.
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When the same code word size is assigned to each pixel, coding redundancy is introduced. When compared to other pixels, some pixel values have a higher frequency in typical photographs. It is simple to find repeated instances of each digit in the picture using this storage representation. When a picture is recorded as a matrix, the values range from 0 to 255, and this frequency distribution of pixel values is referred to as a histogram. As a result, these wavelets are ideal for this application. The wavelets transformation has the capacity to efficiently capture fluctuations at odd scales. This forecast based on a single pixel is less meaningful, and redundancy can be eliminated as a result. Practically, the value of each given pixel may be anticipated based on the values of neighbouring pixels. In the majority of natural photographs, the values of neighbouring pixels are tightly connected. There are three main digital image redundancies that occur in most cases, and they are as follows: As a first stage, many forms of redundancy must be removed before compressing the photos. Uncompressed photos need more bandwidth for transmission and take longer to transmit, and if compressed before storage and transmission, the size of the image may be proportionate to its size. The overall storage needs become higher as the quantity of photos to be kept or images to be delivered grows. The raw data for single coloured photos does not require a lot of storage space. Pseudo code of the encoding algorithm: n ← the bit depth The compression ratio is defined as follows: Cr = n1/n2 Figure 1.2 depicts the whole compression flow. Image compression occurs when the entire data quantity of the bit-stream is smaller than the total data quantity of the original image. The encoded bit-stream is subsequently received by the decoder, which decodes it to produce the decoded picture. When the encoder gets the original picture file, it converts it into a bit-stream, which is a sequence of binary data. Consider the encoder and decoder depicted in Figure 1.2. The goal of image compression coding is to save the picture as little as feasible in a bitstream and to display the decoded image as accurately as possible on the monitor. Now, let’s have a look at what is Image Compression Coding? The fundamental purpose of such a system is to decrease the amount of data stored as much as feasible, so that the decoded image presented on the monitor is as close to the original as possible. The block diagram of the generic image storage system is shown in Figure 1.1. The goal of picture compression is to eliminate image redundancy and store or transfer data in a more efficient manner. Image compression is a type of data compression in which the original image is encoded with a small number of bits. The purpose of image compression is to eliminate image redundancy while also increasing storage capacity for well-organized communication. Compression is beneficial because it reduces the usage of costly resources like hard disc space and transmission bandwidth.Ĭompression focuses on reducing image size without sacrificing the uniqueness and information included in the original. In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits or other information-bearing units than an unencoded representation. As a result, image compression theory is becoming increasingly important for minimising data redundancy and saving more hardware space and transmission bandwidth. In recent years, the development and demand for multimedia products has accelerated, resulting in network bandwidth and memory device storage shortages. RNN Based Encoder and Decoders for Image Compression.In this article, we will discuss Image Compression application in depth involving Machine Learning Techniques like RNN based Encoder and Decoder and applications of Image Compression.