Vector representation: Initially, the image is divided into a non-overlappedīlocks of dimension HxW (for examples 2x2, ). That it becomes suitable to be applied by the GAVQ algorithm. Preparing step, which involves the representation of the problem in such a way Problem representation: The first step of the GAVQ algorithm is the Of the algorithm at each generation and termination criteria, if the terminationĬriteria are satisfied then STOP, otherwise GOTO Step 2). Performance Evaluation and Termination Criteria (testing the performance Merging (merging those sets becomes nearest after performing the genetic The elements of the sets that are produced from Step 2). Genetic Operations (performing the Crossover and Mutation operations on
![vector td x 2 vector td x 2](https://i.ytimg.com/vi/ydKWhnbFPBY/hqdefault.jpg)
Problem Representation (focusing on the choice of a suitableĬlustering (grouping the most similar input instances in sets that haveĬommon characteristics between these input instances). Of Genetic Algorithm (GA) and then uses these facilities to enhance the use This algorithm tries to exploit the facilities
![vector td x 2 vector td x 2](https://cdn.statically.io/img/apkmody.io/wp-content/uploads/2020/03/Vector-2-Premium-upgrade-kits-1024x576.jpg)
VQ using GA (GAVQ): A proposed algorithm: In this section, a new algorithmįor Image compression is suggested. Research efforts inĬodebook design have been concentrated in two directions: to generate a betterĬodebook that approaches the global optimal solution and to reduce the computational Local optimal codebook and needs intensive computation. Method*LBG algorithm is affected by the initial codebook, often generates the More effect on the compression performance. Codebook design is the key problem of VQ and the generated codebook has Methods need long runtime because candidate solutions must be fine tuned by However, most conventional GA-based codebook design Genetic Algorithm (GA) has been successfully applied to codebook design for In the past, programmers might have crafted a special-purpose program for each problem now they can reduce their time significantly by using a genetic algorithm (GA) (Al-Rawi and Stephan, 1999 Grant, 1995 Ryu and Eick, 1995 Louis, 1997 Ou and Chen, 2006). Its easy to find examples: finding the shortest path connecting a set of cities, dividing a set of different tasks among a group of people to meet a deadline, or fitting a set of various sized boxes into the fewest trucks. A problem may qualify as difficult for a number of different reasons for example, the data may be too noisy or irregular the problem may be difficult to model or it may simply take too long to solve. Compression is obtained because transmitting the address of a codebook entry requires fewer bits than transmitting the vector itself (Midanda-Trigueros et al., 1999 Huang et al., 1992 Nasrabadi and King, 1988 Kumar, 1999).Ī surprising number of everyday problems are difficult to solve by traditional algorithms. The address of the codebook entry most similar to the signal vector is then transmitted to the receiver, where it is used to fetch the same entry from an identical codebook, thus reconstructing an approximating to the original signal. Each vector of the signal to be compressed is compared to the entries of a codebook containing representative vectors. In its simplest implementation, VQ requires breaking the signal to be compressed into vectors (which may be referred to as a blocks). Data compression using Vector Quantization (VQ) has received great attention in the last decade of its promising compression ratio and relatively simple structure. However, despite more than two decades of intensive research, VQs theoretical promise is yet to be fully realized in image compression practice (Wu and Wen, 1999). Vector Quantization (VQ) is a source coding methodology with a provable rate-distortion optimality. Often, the distortion measure is simply the mean squared error between the quantized pixels and the codevector (Cabral, 1994 Lin and Chang, 2006). The appropriate codeword is chosen from the available codebook by minimizing a given distortion measure.
![vector td x 2 vector td x 2](https://static.vecteezy.com/system/resources/previews/010/168/697/large_2x/letter-d-or-td-monogram-logo-with-business-card-design-vector.jpg)
Vector Quantization can be used to take advantage of the correlation between neighboring pixels by quantizing pixels in groups (or vectors) rather than individually and symbolically representing the vector with a codeword. One of the emerging technologies for lossy data (image in this research) compression is Vector Quantization (VQ).
![vector td x 2 vector td x 2](https://get.wallhere.com/photo/1920x1080-px-black-background-Half-Life-2-orange-Portal-2-Portal-Gun-Pulp-Fiction-parody-vectors-video-games-655155.jpg)
Vector Quantization (VQ) has been successfully used in speech and image data compression (Liang et al., 1995). Since the codebook can be pre-generated before the encoding process, the efficiency of the vector lookup is comparatively more significant (Chan et al., 1994 Hwang et al., 2001). However, the computational complexity of both the codebook design and the vector lookup during the encoding phase is obviously a burden of its realization. Vector Quantization (VQ) has been widely used for data compression due to its theoretical advantages compared to scalar quantization schemes.